CN112435496A - Vehicle and ship intelligent navigation control early warning device and method based on multiple sensors - Google Patents
Vehicle and ship intelligent navigation control early warning device and method based on multiple sensors Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G3/00—Traffic control systems for marine craft
Abstract
The invention relates to a vehicle and ship intelligent navigation control early warning device and a method based on multiple sensors, belonging to the technical field of intelligent safety auxiliary driving, comprising a GPS positioning module, an industrial camera, an industrial personal computer and an electric control unit module, wherein the invention adopts the fusion positioning technology of inertial navigation, GPS combination and a vehicle-mounted sensor (camera), reduces the dependence on high-precision maps to a certain extent, solves the problem of short-term loss of satellite navigation signals, realizes high-precision seamless positioning of automobiles and ships, realizes the switching between the man-vehicle cooperative driving mechanism and the driving control right under the condition of deviating from a preset channel, meanwhile, the defects that the positioning error is accumulated along with time, the signal is easy to lose the target, the influence of factors such as weather environment is large and the like in the current single sensor navigation control method are overcome, the information of the sensors can be integrated by adopting multiple sensors, and higher navigation positioning performance can be obtained.
Description
Technical Field
The invention belongs to the technical field of intelligent safety auxiliary driving, and particularly relates to a vehicle and ship intelligent navigation control early warning device and method based on multiple sensors.
Background
Driven by the trends of global automobile power electrification, control intellectualization and information networking, an Intelligent Transportation System (ITS) has become a popular research field in many countries. Automatic driving becomes a leading hotspot in the field of artificial intelligence and a competitive core of future markets, automobile and ship intellectualization becomes the largest hotspot in the world, and unmanned driving is the ultimate goal of intelligent driving technology. The research and development of the unmanned intelligent vehicle and the ship are expected to reduce traffic accidents and reduce casualty rate of the traffic accidents.
Currently, there are three main techniques for locating a moving object: independent positioning, ground radio positioning, satellite positioning, ground radio positioning signal is easily disturbed by ground obstacles, thereby signal attenuation and multipath effect are generated, and positioning accuracy is reduced or fails. Therefore, terrestrial radiolocation techniques are currently rarely applied to land vehicle positioning. The satellite positioning precision is high, the real-time positioning speed is high, the anti-interference performance is good, the secrecy capability is strong, and the satellite positioning system can be used for positioning and measuring the speed of various users on the sea, in the air, on the land and the like, but in a sheltered position, a signal is easy to lose a target. An independently located inertial navigation system is an autonomous navigation system that does not rely on external information, nor radiates energy to the outside. The device is completely autonomous, does not need to use communication equipment, is slightly influenced by external factors, and has working environments including air and ground and underwater, but has a positioning error accumulation effect.
Therefore, there is a need in the art for a new solution to solve this problem.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the vehicle and ship intelligent navigation control early warning device and method based on the multiple sensors are provided, an inertial navigation and GPS combination and vehicle-mounted sensor (camera) fusion positioning technology is adopted, dependence on a high-precision map is reduced to a certain extent, the problem of short-time missing of satellite navigation signals is solved, high-precision seamless positioning of a vehicle and a ship is achieved, switching between a man-vehicle cooperative driving mechanism and a driving control right is achieved aiming at the condition of deviating from a preset channel, meanwhile, the defects that positioning errors are accumulated along with time, signals are prone to losing targets, influences of factors such as weather environments are large and the like in the current single sensor navigation control method are overcome, sensor information can be integrated by adopting the multiple sensors, and higher navigation positioning performance can be obtained.
Vehicle boats and ships intelligent navigation control early warning device based on multisensor, characterized by: the system comprises a GPS positioning module, an industrial camera, an industrial personal computer and an electric control unit module, wherein the signal output end of the GPS positioning module is connected with the industrial personal computer, the signal output end of the industrial personal computer is connected with the electric control unit module, and the electric control unit module comprises a real-time state information acquisition module, a driving track generation module, a path tracking module, a deviation preset track detection module, a danger target detection module and an early warning module; the output end of the state information acquisition module is connected with the driving track generation module, the output end of the driving track generation module is connected with the path tracking module, the output end of the path tracking module is connected with the deviation preset track detection module, and the output ends of the deviation preset track detection module and the danger target detection module are connected with the early warning module; and the signal output end of the industrial camera is respectively connected with the industrial personal computer and the dangerous target detection module.
The GPS positioning module comprises a satellite line number I, a base station antenna, a land control room power supply, a voltage transformation inverter I, a base station radio station antenna, a shipborne mobile station radio station antenna, a marine ship control room power supply, a voltage transformation inverter II, a shipborne mobile station radio station, a shipborne mobile station, a satellite line number II and a shipborne mobile station antenna; the land control room power supply is connected with a base station and a base station radio station through a voltage transformation inverter I, the base station radio station is connected with a base station radio station antenna, and the base station antenna is connected with the base station; the power supply of the marine ship control room is connected with a shipborne mobile station radio station and a shipborne mobile station through a voltage transformation inverter II, a shipborne mobile station radio station antenna is connected with the shipborne mobile station radio station, a shipborne mobile station antenna is connected with the shipborne mobile station, and the shipborne mobile station is connected with an industrial personal computer.
The early warning module is externally connected with a display screen, a prompting lamp device and a sound device of the device.
The vehicle and ship intelligent navigation control early warning method based on the multiple sensors is characterized by comprising the following steps: the vehicle and ship intelligent navigation control early warning device based on the multiple sensors comprises the following steps,
step one, establishing a real-time position state information acquisition module
Acquiring position state information of the vehicle and the ship through a GPS positioning module, calculating the relative position of the vehicle and the ship in an inertial coordinate system, generating a driving track map in global mapper mapping software through coordinate conversion, and acquiring a satellite positioning map of the vehicle and the ship;
combining an automobile two-degree-of-freedom dynamic model with vehicle CAN bus information, and performing position calculation by using an inertial system to obtain state information of a vehicle and a ship;
step two, generating a reference track
Acquiring microscopic information of the vehicle and the ship by using an industrial camera, wherein the microscopic information comprises position coordinates, speed and course angle, and storing the microscopic information in a data form to obtain a reference track database;
step three, intelligent navigation path tracking control
Performing feedback optimization processing according to the reference track database obtained in the step two and the vehicle and ship state parameters obtained in the step one, and performing track tracking through a driving track generation module to complete path planning; the electric signal is transmitted to a vehicle and ship executing mechanism through a controller model to carry out intelligent navigation control;
step four, intelligently assisting driving and early warning of dangerous conditions
Detecting that the driving path has an obstacle by using an industrial camera, and planning a new driving path according to the reference track database of the step two and by combining the state information of the vehicle and the ship of the step one; the GPS positioning module is used for positioning abnormity, the intelligent navigation control deviates from a preset running track, the early warning module adopts a prompting lamp device and a sound device of the device to carry out early warning, and a running path is planned again based on a convolutional neural network deep learning model simulation decision.
The formula for carrying out position estimation through an inertial system in the step one is as follows,
wherein (x)0,y0) The initial position of the vehicle at time 0, di is the distance traveled by the odometer, [ theta ]iIs the course angle value obtained by the inertial navigation system, (x)n,yn) Is the estimated real-time position of the vehicle.
And the optimization feedback processing of the third step adopts a linear model prediction control algorithm to generate a linear time-varying model prediction controller for predicting a model, performing rolling optimization and performing feedback correction.
Through the design scheme, the invention can bring the following beneficial effects: the vehicle and ship intelligent navigation control early warning device and method based on the multiple sensors establish a real-time vehicle and ship state information acquisition module, generation of a reference track, design of an autonomous tracking navigation controller, deviation from a preset channel and detection early warning driving decision when a dangerous target is encountered, and adopt an inertial navigation, GPS combination and vehicle-mounted sensor (camera) fusion positioning technology to ensure that high-precision positioning can still be output under the condition of navigation signal loss.
The invention has the further beneficial effects that:
1. the invention has short signal processing time inside the module and between modules, and can meet the requirement of real-time property.
2. The invention improves the accuracy of acquiring the state information of the vehicle and the ship, can play a good early warning effect on the deviation of the reference track and the running in case of meeting an obstacle, and provides technical support reference for the research and development of unmanned intelligent vehicles and ships.
Drawings
The invention is further described with reference to the following figures and detailed description:
FIG. 1 is a block diagram showing the structure of the intelligent navigation control early warning device for vehicles and ships based on multiple sensors.
FIG. 2 is a schematic block diagram of a flow of the vehicle and vessel intelligent navigation control early warning method based on multiple sensors.
In the figure, 1-satellite line number I, 2-base station antenna, 3-land control room power supply, 4-transformation inverter I, 5-base station, 6-base station radio station, 7-base station radio station antenna, 8-shipborne mobile station radio station antenna, 9-marine ship control room power supply, 10-transformation inverter II, 11-shipborne mobile station radio station, 12-shipborne mobile station, 13-industrial personal computer, 14-satellite line number II, 15-shipborne mobile station antenna, 16-industrial camera, 17-real-time state information acquisition module, 18-driving track generation module, 19-path tracking module, 20-deviation preset track detection module, 21-dangerous target detection module, 22-early warning module, 23-prompting lamp equipment, power supply, and the like, 24-the device itself audio equipment, 25-speakers, 26-the display screen.
Detailed Description
As shown in fig. 1, the dotted line in the figure represents wireless transmission, and the vehicle and ship intelligent navigation control early warning device comprises a GPS positioning module, an industrial camera 16, an industrial personal computer 13 and an electronic control unit module, wherein a signal output end of the GPS positioning module is connected with the industrial personal computer 13, a signal output end of the industrial personal computer 13 is connected with the electronic control unit module, and the electronic control unit module comprises a real-time state information acquisition module 17, a driving track generation module 18, a path tracking module 19, a deviation preset track detection module 20, a danger target detection module 21 and an early warning module 22; the output end of the state information acquisition module 17 is connected with the driving track generation module 18, the output end of the driving track generation module 18 is connected with the path tracking module 19, the output end of the path tracking module 19 is connected with the deviation preset track detection module 20, and the output ends of the deviation preset track detection module 20 and the danger target detection module 21 are connected with the early warning module 22; and the signal output end of the industrial camera 16 is respectively connected with the industrial personal computer 13 and the dangerous target detection module 21.
The GPS positioning module comprises a satellite line number I1, a base station antenna 2, a land control room power supply 3, a voltage transformation inverter I4, a base station 5, a base station radio station 6, a base station radio station antenna 7, a shipborne mobile station radio station antenna 8, a marine ship control room power supply 9, a voltage transformation inverter II10, a shipborne mobile station radio station 11, a shipborne mobile station 12, a satellite line number II 14 and a shipborne mobile station antenna 15; the land control room power supply 3 is connected with a base station 5 and a base station radio station 6 through a voltage transformation inverter I4, the base station radio station 6 is connected with a base station radio station antenna 7, and the base station antenna 2 is connected with the base station 5; the marine vessel control room power supply 9 is connected with a shipborne mobile station radio station 11 and a shipborne mobile station 12 through a voltage transformation inverter II10, a shipborne mobile station radio station antenna 8 is connected with the shipborne mobile station radio station 11, a shipborne mobile station antenna 15 is connected with the shipborne mobile station 12, and the shipborne mobile station 12 is connected with an industrial personal computer 13.
The early warning module 22 is externally connected with a display screen 26, a prompting lamp device 23 and a device sound device 24, and the device sound device 24 is externally connected with a loudspeaker 25.
The method for controlling and early warning the intelligent navigation of the vehicle and the ship based on the multiple sensors, as shown in figure 2, comprises the following steps,
step one, establishing a real-time vehicle and ship position state information acquisition module
I inertial navigation positioning coordinate conversion process
Firstly, state information of the vehicle and the ship is acquired through a navigation sensor, an inertial sensor measures information of speed, acceleration, displacement, course and the like of a carrier, and the relative position of the carrier in an inertial coordinate system is calculated. The raw data collected by the navigation sensor is the longitude and latitude and height coordinate values of the center of the navigator in the WGS-84 coordinate system. And transforming the vehicle position described by the latitude and longitude and elevation information of the ship on the earth surface into a local horizontal coordinate system through coordinate transformation.
The coordinate transformation is to transform the geocentric geostationary space coordinate system to the geocentric geostationary rectangular coordinate system, and the geocentric geostationary rectangular coordinate system is transformed to the local horizontal coordinate system. And the geocentric geostationary space coordinate system is converted into a geocentric geostationary rectangular coordinate system, and the spherical coordinates represented by the longitude and latitude elevation information are converted into space rectangular coordinates. The earth-centered earth-fixed rectangular coordinate system is converted into a local horizontal coordinate system, and the earth-centered earth-fixed rectangular coordinate system can be realized through certain rotation and translation.
And secondly, through a coordinate conversion step, extracting longitude and latitude information acquired by the navigation sensor in the driving process, converting the longitude and latitude information into csv files, importing the csv files into global mapper software to generate a driving track map, realizing high-precision positioning of a vehicle and ship driving area in a satellite map, and providing real-time and continuous vehicle position estimation through a positioning module so that a system can correctly distinguish the current driving road section of the vehicle and the approaching road position.
II vehicle and ship running state and key parameter acquisition thereof
The method is characterized by comprising the following steps of firstly, researching an online real-time estimation algorithm of parameters such as vehicle speed, vehicle body inclination angle and road gradient on the basis of a two-degree-of-freedom dynamic model of the vehicle and combining information such as a high-precision positioning GPS/inertial navigation system and the running state of the whole vehicle.
In the aspect of absolute positioning, model switching and an interactive multi-model algorithm are adopted to fuse high-precision positioning, GPS/INS, a vehicle CAN bus and other information, a plurality of different vehicle dynamics models are used for matching vehicle running states and parameters, and each model is subjected to interactive operation according to a certain rule and then outputs a filtering value, so that the state estimation precision and the self-adaptive capacity are improved.
Secondly, the online identification of the state parameters of the vehicle and the ship is realized, and the data of the running track, including navigation information such as the position coordinate of the vehicle (ship), the running speed, the course angle and the like, can be recorded in real time. The dead reckoning is realized by using an inertial system, and the lower graph shows the reckoning process of the track, wherein the formula is shown as follows (x)0,y0) The initial position of the vehicle at time 0, di is the distance traveled by the odometer, [ theta ]iIs the course angle value obtained by the inertial navigation system, (x)n,yn) Is the estimated real-time position of the vehicle.
III high-precision GPS navigation positioning principle
The GPS satellite positioning method can continuously acquire positioning information of vehicles and ships all day long by adopting GPS satellite positioning, and determines the position of a carrier by signals transmitted by a navigation positioning satellite, and the position of a certain point in the earth or space can be determined by longitude, latitude and altitude. In addition, there is an unknown clock difference, which is the difference between the satellite clock and the receiver clock. If at some time the GPS receiver is able to acquire four satellites, the four unknowns can be resolved to obtain location information. The GPS positioning principle is shown by the following formula
L＝tR×c
Wherein, tRThe time delay of the signal from the satellite to the receiver, c is the speed of light, and L is the distance between the satellite with the known position and the vehicle-mounted receiver.
The inertial navigation positioning and the all-weather vehicle GPS navigation positioning are combined, are mutually independent and mutually detect the effectiveness, so that the vehicle and ship state information is accurately acquired, and the positioning reliability is improved.
Step two, generating a reference track
Firstly, reference track information needs to be collected in advance to realize the navigation control task of an automatic driving vehicle (ship). In order to realize the reference track data acquisition process, it is necessary to ensure that the differential signal coverage transmitted by the base station radio station is large enough, so that the base station is placed at a position with a wide terrain and a high altitude, and the mobile station is installed at the central position of a vehicle or a ship carrier and moves along with the carrier, so as to be convenient for receiving GPS satellite signals. And after the installation and debugging of the reference station equipment are completed, carrying out a data acquisition experiment. The macro position information and the speed information collected by the sensor sensors of the base station and the vehicle-mounted and shipborne mobile station are fused, and the result shows that the data after the information fusion can meet the high precision requirement.
And secondly, the microscopic information acquired by the industrial camera is matched to accurately position the ship, and the ship is assisted to realize autonomous navigation in a preset channel by using surrounding geographic information in real time. The method comprises the steps of fusing macroscopic information and microscopic information to enable navigation and driving of vehicles and ships to be more accurate, obtaining needed ship position and attitude parameters through coordinate conversion of the obtained information, completing real-time collection of navigation information such as vehicle (ship) position coordinates, driving speed, course angle and the like in the vehicle driving process through data collection of a navigator and a coordinate conversion program, storing collected change information of the parameters such as the vehicle and ship position coordinates, speed, course angle and the like in a database mode, and recording related reference tracks to form a reference track database. As a source of a tracking control reference trajectory for the unmanned vehicle.
And thirdly, predicting and outputting future vehicle and ship track information according to the track information at the moment on the ship and the input currently acquired vehicle and ship pose information, continuously and repeatedly optimizing, and generating a reference track. According to the reference track, a vehicle unmanned tracking control algorithm can be designed.
Step three, designing an autonomous intelligent navigation controller
The design of the vehicle self-pilot navigation controller aims to control the vehicle to run along a planned path, and mainly comprises the transverse control and the longitudinal control of the vehicle. In order to realize the motion navigation control of the unmanned vehicle, the control of a two-degree-of-freedom dynamic model of the vehicle (ship) is required to realize the motion navigation control, so that the vehicle can quickly and stably track to a desired track.
Secondly, in the control calculation process of the vehicle (ship), an approximate linearization method is often adopted to carry out linearization processing on the vehicle model. In order to design a controller by adopting a linear model predictive control algorithm, the linear time-varying model predictive controller is designed, comprises 3 steps of predicting a model, rolling optimization and feedback correction, and can accurately track the running of a ship.
Thirdly, according to the generated reference track, the obtained vehicle and ship state parameters are subjected to feedback optimization processing, and an unmanned track controller is designed. And controlling the steering angle of the vehicle and the ship, transmitting the electric signal to a steering actuating mechanism through a controller model, and controlling the steering of the actual unmanned vehicle and the ship. And tracking the track by using the established reference track database and the designed controller to complete path planning.
And fourthly, under the condition of low speed, tracking a straight track and a circular track, starting from a given initial state of the vehicle in an inertial coordinate system, wherein the position of the initial state can be on a desired reference track or not, and parameters such as the wheel rotation angle and the rotating speed of the vehicle (ship) are controlled according to the deviation between the state of the reference track and the state of the actual vehicle in the running process of the vehicle, so that the unmanned vehicle can track the desired track along the desired speed.
Step four, deviation from a preset channel and detection early warning driving decision of dangerous target
Firstly, when the navigation signal is influenced due to special reasons, the navigation positioning precision is reduced, and the deviation from the preset running track is caused, and the early warning module adopts an alarm lamp and a sound horn for early warning. And (3) carrying out supervised learning based on a convolutional neural network deep learning model and limited driving behavior data of a skilled driver, and training a personified driving decision system to realize the personified driving decision and navigation of the skilled driver. The autonomous decision and the switching of the control right of the man vehicle (ship) are realized, the intelligent driving decision is realized, the safe driving is not influenced, and the switching of the man vehicle cooperative driving mechanism and the driving control right is realized.
Secondly, when the camera detects that the surrounding obstacles exist in the navigation and driving process of the unmanned vehicle (ship), the navigation controller plans a new driving path for the vehicle again according to the reference track database by combining the actual driving process and the surrounding environment perception. The method is characterized in that an anthropomorphic driving cognition decision-making system is trained and learned based on limited driving data of a skilled driver, brain learning and optimized modeling of intelligent driving are achieved, and then automatic driving behavior decision simulating the skilled driver is established by combining knowledge such as a driving map, driving experience and path planning, and autonomous decision making is completed.
And the vehicle-mounted onboard computer system provides a man-machine interaction interface, and displays information required by users, such as vehicle positions, optimal path planning results, real-time driving guide instructions and the like with a digital map as a background in prompt modes, such as voice prompt, visual graphs and the like. The driver timely responds to the co-driving mechanism of the other vehicles in cooperation and the visual and efficient human-computer interaction interface HMI, a driving control right switching algorithm for switching from full automatic driving to driver takeover control is established according to real-time road conditions, surrounding situations, vehicles and driver states, and comfortableness, safety and stability of the switching process are guaranteed.
Selecting a typical structured road scene to perform performance demonstration, testing by combining subjective riding evaluation, and objectively testing by adopting subjective feeling evaluation of experts and drivers and algorithm real-time performance. An autonomous decision index: the accuracy rate of the generated dynamic travelable path is more than or equal to 95 percent, the time for completing one path planning is less than or equal to 100 milliseconds, the vehicle is controlled to travel according to the planned path, and the accuracy rate of the driving operation decision is more than or equal to 95 percent; the switching accuracy of the intelligent driving control right is more than 90%, and the satisfaction rate of the driver on the driving right distribution under the driving state is not lower than 85%.
Claims (6)
1. Vehicle boats and ships intelligent navigation control early warning device based on multisensor, characterized by: the system comprises a GPS positioning module, an industrial camera (16), an industrial personal computer (13) and an electric control unit module, wherein the signal output end of the GPS positioning module is connected with the industrial personal computer (13), the signal output end of the industrial personal computer (13) is connected with the electric control unit module, and the electric control unit module comprises a real-time state information acquisition module (17), a driving track generation module (18), a path tracking module (19), a deviation preset track detection module (20), a danger target detection module (21) and an early warning module (22); the output end of the state information acquisition module (17) is connected with the driving track generation module (18), the output end of the driving track generation module (18) is connected with the path tracking module (19), the output end of the path tracking module (19) is connected with the deviation preset track detection module (20), and the output ends of the deviation preset track detection module (20) and the danger target detection module (21) are connected with the early warning module (22); and the signal output end of the industrial camera (16) is respectively connected with the industrial personal computer (13) and the dangerous target detection module (21).
2. The intelligent navigation control early warning device for the vehicle and the ship based on the multiple sensors as claimed in claim 1, wherein: the GPS positioning module comprises a satellite line number I (1), a base station antenna (2), a land control room power supply (3), a transformation inverter I (4), a base station (5), a base station radio station (6), a base station radio station antenna (7), a shipborne mobile station radio station antenna (8), a marine ship control room power supply (9), a transformation inverter II (10), a shipborne mobile station radio station (11), a shipborne mobile station (12), a satellite line number II (14) and a shipborne mobile station antenna (15); the land control room power supply (3) is connected with a base station (5) and a base station radio station (6) through a voltage transformation inverter I (4), the base station radio station (6) is connected with a base station radio station antenna (7), and the base station antenna (2) is connected with the base station (5); the marine ship control room power supply (9) is connected with a shipborne mobile station radio station (11) and a shipborne mobile station (12) through a voltage transformation inverter II (10), a shipborne mobile station radio station antenna (8) is connected with the shipborne mobile station radio station (11), a shipborne mobile station antenna (15) is connected with the shipborne mobile station (12), and the shipborne mobile station (12) is connected with an industrial personal computer (13).
3. The intelligent navigation control early warning device for the vehicle and the ship based on the multiple sensors as claimed in claim 1, wherein: the early warning module (22) is externally connected with a display screen (26), a prompting lamp device (23) and a sound device (24) of the device.
4. The vehicle and ship intelligent navigation control early warning method based on the multiple sensors is characterized by comprising the following steps: the intelligent navigation control early warning device for the vehicle and the ship based on the multiple sensors, which is applied to the intelligent navigation control early warning device for the vehicle and the ship based on the multiple sensors, comprises the following steps,
step one, establishing a real-time position state information acquisition module
Acquiring position state information of the vehicle and the ship through a GPS positioning module, resolving (obtaining the relative position of the vehicle and the ship in an inertial coordinate system, generating a driving track map in global mapper mapping software through coordinate conversion, and acquiring a satellite positioning map of the vehicle and the ship;
combining an automobile two-degree-of-freedom dynamic model with vehicle CAN bus information, and performing position calculation by using an inertial system to obtain state information of a vehicle and a ship;
step two, generating a reference track
Acquiring microscopic information of the vehicle and the ship by using an industrial camera (16), wherein the microscopic information comprises position coordinates, speed and course angle, and storing the microscopic information in a data form to obtain a reference track database;
step three, intelligent navigation path tracking control
Performing feedback optimization processing according to the reference track database obtained in the step two and the vehicle and ship state parameters obtained in the step one, and performing track tracking through a driving track generation module (18) to complete path planning; the electric signal is transmitted to a vehicle and ship executing mechanism through a controller model to carry out intelligent navigation control;
step four, intelligently assisting driving and early warning of dangerous conditions
Detecting that the driving path has an obstacle by using an industrial camera (16), and planning a new driving path according to the reference track database of the step two and by combining the state information of the vehicle and the ship of the step one; the GPS positioning module is used for positioning abnormity, the intelligent navigation control deviates from a preset running track, the early warning module (22) adopts a prompting lamp device (23) and a sound device (24) of the device to carry out early warning, and a running path is planned again based on a convolutional neural network deep learning model simulation decision.
5. The intelligent navigation control early warning method for the vehicle and the ship based on the multiple sensors, which is characterized in that: the formula for carrying out position estimation through an inertial system in the step one is as follows,
wherein (x)0,y0) The initial position of the vehicle at time 0, di is the distance traveled by the odometer, [ theta ]iIs the course angle value obtained by the inertial navigation system, (x)n,yn) Is the estimated real-time position of the vehicle.
6. The intelligent navigation control early warning method for the vehicle and the ship based on the multiple sensors, which is characterized in that: and the optimization feedback processing of the third step adopts a linear model prediction control algorithm to generate a linear time-varying model prediction controller for predicting a model, performing rolling optimization and performing feedback correction.
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