CN116001818A - Control system and method for unmanned mode of automobile - Google Patents
Control system and method for unmanned mode of automobile Download PDFInfo
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- CN116001818A CN116001818A CN202310007953.XA CN202310007953A CN116001818A CN 116001818 A CN116001818 A CN 116001818A CN 202310007953 A CN202310007953 A CN 202310007953A CN 116001818 A CN116001818 A CN 116001818A
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Abstract
The invention relates to the technical field of unmanned vehicles and discloses a control system and a control method for an unmanned vehicle mode. The behavior data acquisition module is connected with the central control module and the decision execution module through the CAN bus, receives characteristic information of the decision execution module in a manual driving mode, acquires optimal behavior parameters under different driving conditions by constructing the artificial neural network model, performs data fusion on the acquired optimal behavior parameters and execution feedback information, and generates a decision instruction with human driving characteristics for controlling the decision execution module, so that a control result and a control process of the decision execution module are more similar to the manual driving mode, are matched with driving habits of human beings, and CAN improve experience of passengers.
Description
Technical Field
The invention relates to the technical field of unmanned automobile driving, in particular to a control system and method for an unmanned automobile driving mode.
Background
The transverse and longitudinal control systems of the unmanned vehicle are the basis for the stable and safe operation of the unmanned system. The existing control systems of unmanned vehicles mostly adopt closed-loop control systems based on error feedback. The principle is that errors are rapidly controlled to be in a zero state through a high-precision mathematical analysis model, but the errors are not very sensitive to small errors in the actual driving process of a driver, the driver can take control actions only when the errors are larger than a certain threshold value, and because the driving habits of each driver are different, the control on the driving state of the vehicle is also greatly different, so that the control effect in the automatic driving mode of the control system is difficult to match with the driving habits of a human, passengers feel uncomfortable and even panic emotion is caused, and the experience is poor.
Disclosure of Invention
The invention mainly aims to provide a control system and a control method for an unmanned mode of an automobile, wherein a behavior data acquisition module is connected with a central control module and a decision execution module through a CAN bus, receives characteristic information of the decision execution module in a manual driving mode, acquires optimal behavior parameters under different driving working conditions by constructing an artificial neural network model, performs data fusion on the acquired optimal behavior parameters and execution feedback information, generates a decision instruction with human driving characteristics and is used for controlling the decision execution module, so that a control result and a control process of the decision instruction are more similar to the manual driving mode, the decision execution module is matched with driving habits of human beings, the experience of passengers CAN be improved, and the problems in the background art CAN be effectively solved.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the utility model provides a control system of unmanned mode of car, includes environment perception module, horizontal control module, vertical control module, central control module, action data acquisition module and decision execution module, the environment perception module acquires and handles the decision influence parameter in the course of traveling to with the decision influence parameter of acquireing is passed through the CAN bus and is sent to central control module, central control module generates decision command according to the decision influence parameter of acquireing, and with decision command send to horizontal control module with vertical control module, by horizontal control module with vertical control module sends the execution command to decision execution module, and gathers the execution feedback information feedback of decision execution module arrives central control module realizes unmanned mode's closed loop control, its characterized in that: the behavior data acquisition module is connected with the central control module and the decision execution module through a CAN bus, receives characteristic information of the decision execution module in a manual driving mode, acquires optimal behavior parameters under different driving working conditions by constructing an artificial neural network model, and performs data fusion on the acquired optimal behavior parameters and execution feedback information to generate a decision instruction with human driving characteristics.
Furthermore, the environment sensing module comprises a camera, a laser radar, a millimeter wave radar and an INS/GPS integrated navigation system.
Further, the decision execution module comprises an accelerator control mechanism, a brake control mechanism, a gear control mechanism and a direction control mechanism.
Further, the behavior data acquisition module comprises a singlechip, an accelerator opening sensor, a brake pressure sensor, a gear information acquisition device and a steering wheel angle sensor.
Further, the cameras are distributed at the vehicle roof or the vehicle windshield and used for acquiring lane center line, traffic signal lamps and traffic sign information, the laser radars are uniformly distributed around the vehicle body and used for acquiring distance, azimuth and profile data of static obstacles, the millimeter wave radars are fixedly arranged at the middle position of the top of the vehicle and used for acquiring ID, azimuth, distance and speed of dynamic obstacles around the vehicle, and the INS/GPS integrated navigation system is used for acquiring current longitude, latitude, heading angle, speed, pitch angle, roll angle and GPS time information of the vehicle.
Further, the characteristic information is an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving conditions in the manual driving mode.
Further, the driving working conditions comprise a following working condition, a cruising working condition, a lane changing working condition and an obstacle avoidance working condition.
Further, the execution feedback information is an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving conditions in the unmanned mode.
Furthermore, the artificial neural network model takes characteristic information normalization indexes under different driving conditions in an artificial driving mode as an input vector X= (X) of the neural network i,j ) Taking the normalization index of the execution feedback information under different driving conditions in the unmanned mode as a target output vector Y= (Y) i ) The hidden layer takes 6 neurons, transfer functions of the hidden layer and the output layer are respectively selected as Sigmoid and Pureline functions, and the training times are not less than 1000 times.
Further, the control method of the control system comprises the following steps:
step one: acquiring an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving conditions in a manual driving mode and an unmanned driving mode respectively;
step two: normalized indexes of accelerator opening sensor values, brake pressure sensor values, gear information and steering wheel rotation angle sensor values under different driving working conditions in a manual driving mode are used as input vectors X= (X) of a neural network i,j ) Normalized indexes of accelerator opening sensor values, brake pressure sensor values, gear information and steering wheel rotation angle sensor values under different driving working conditions in an unmanned mode are used as target output vectors Y= (Y) i ) The hidden layer takes 6 neurons, transfer functions of the hidden layer and the output layer are respectively selected as Sigmoid and Pureline functions, the training times are not less than 1000 times, the function relation between an input vector and an output vector is obtained, and the optimal behavior parameters under different running working conditions are obtained through the function relation;
step three: performing data fusion on the acquired optimal behavior parameter data and execution feedback information to generate a decision instruction with human driving characteristics;
step four: the environment sensing module acquires and processes decision influencing parameters in the driving process, the acquired decision influencing parameters are sent to the central control module through a CAN bus, the central control module generates a decision instruction with human driving characteristics according to the acquired decision influencing parameters, the decision instruction is sent to the transverse control module and the longitudinal control module, the transverse control module and the longitudinal control module send execution instructions to the decision execution module, the throttle control mechanism of the decision execution module controls the opening degree of an accelerator, the brake control mechanism controls the brake pressure, the gear control mechanism controls the gear, the steering wheel corner is controlled through the direction control mechanism, and the execution feedback information of the decision execution module is collected and fed back to the central control module.
Compared with the prior art, the invention has the following beneficial effects:
(1) The system comprises a central control module, a decision execution module, a behavior data acquisition module, a CAN bus, a control module, a decision execution module, a control result and a control process, wherein the behavior data acquisition module is connected with the central control module and the decision execution module through the CAN bus, receives characteristic information of the decision execution module in a manual driving mode, takes normalized indexes of accelerator opening sensor values, brake pressure sensor values, gear information and steering wheel angle sensor values under different driving conditions as input vectors of a neural network by constructing the normalized indexes of the accelerator opening sensor values, the brake pressure sensor values, the gear information and the steering wheel angle sensor values under different driving conditions in the manual driving mode, takes normalized indexes of the accelerator opening sensor values, the brake pressure sensor values, the gear information and the steering wheel angle sensor values under different driving conditions as target output vectors in the unmanned driving mode, acquires optimal behavior parameters under different driving conditions, performs data fusion on the acquired optimal behavior parameters and execution feedback information, generates a decision instruction with human driving characteristics for controlling the decision execution module, enables the control result and the control process to be closer to the manual driving mode, is matched with the driving habit of human beings, and the experience of passengers CAN be improved.
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FIG. 1 is a schematic diagram of the overall structure of a control system in an unmanned mode of an automobile according to the present invention;
fig. 2 is a schematic diagram of the overall structure of a control system in an unmanned mode of an automobile in a manual driving mode.
Detailed Description
The present invention will be further described with reference to the following detailed description, wherein the drawings are for illustrative purposes only and are presented as schematic drawings, rather than physical drawings, and are not to be construed as limiting the invention, and wherein certain components of the drawings are omitted, enlarged or reduced in order to better illustrate the detailed description of the present invention, and are not representative of the actual product dimensions.
Example 1
As shown in fig. 1, a control system of an unmanned mode of an automobile comprises an environment sensing module, a transverse control module, a longitudinal control module, a central control module, a behavior data acquisition module and a decision execution module, wherein the environment sensing module acquires and processes decision influencing parameters in a driving process, the acquired decision influencing parameters are sent to the central control module through a CAN bus, the central control module generates decision instructions according to the acquired decision influencing parameters and sends the decision instructions to the transverse control module and the longitudinal control module, the transverse control module and the longitudinal control module send execution instructions to the decision execution module, and acquire execution feedback information of the decision execution module to feed back to the central control module, closed-loop control of the unmanned mode is realized, the behavior data acquisition module is connected with the central control module and the decision execution module through the CAN bus, receives characteristic information of the decision execution module in a manual driving mode, acquires optimal behavior parameters under different driving conditions through constructing a manual neural network model, and performs data fusion on the acquired optimal behavior parameters and the execution feedback information, so as to generate the decision instructions with human driving characteristics.
The environment sensing module comprises a camera, a laser radar, a millimeter wave radar and an INS/GPS integrated navigation system.
The decision execution module comprises an accelerator control mechanism, a brake control mechanism, a gear control mechanism and a direction control mechanism.
The behavior data acquisition module comprises a singlechip, an accelerator opening sensor, a brake pressure sensor, a gear information acquisition device and a steering wheel angle sensor.
The cameras are distributed at the vehicle roof or the vehicle windshield and used for acquiring lane center lines, traffic signal lamps and traffic sign information, the laser radars are uniformly arranged around the vehicle body and used for acquiring distance, azimuth and profile data of static obstacles, the millimeter wave radars are fixedly arranged at the middle position of the vehicle roof and used for acquiring ID, azimuth, distance and speed of dynamic obstacles around the vehicle, and the INS/GPS integrated navigation system is used for acquiring current longitude, latitude, heading angle, speed, pitch angle, roll angle and GPS time information of the vehicle.
The characteristic information is an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving working conditions in an artificial driving mode.
The driving working conditions comprise a following working condition, a cruising working condition, a lane changing working condition and an obstacle avoidance working condition.
The execution feedback information is the values of an accelerator opening sensor, a brake pressure sensor, gear information and a steering wheel angle sensor under different driving working conditions in the unmanned mode.
The artificial neural network model takes characteristic information normalization indexes under different driving working conditions in an artificial driving mode as an input vector X= (X) of the neural network i,j ) Taking the normalization index of the execution feedback information under different driving conditions in the unmanned mode as a target output vector Y= (Y) i ) The hidden layer takes 6 neurons, transfer functions of the hidden layer and the output layer are respectively selected as Sigmoid and Pureline functions, and the training times are not less than 1000 times.
By adopting the technical scheme: the control method of the control system comprises the following steps:
step one: acquiring an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving conditions in a manual driving mode and an unmanned driving mode respectively;
step two: normalized indexes of accelerator opening sensor values, brake pressure sensor values, gear information and steering wheel rotation angle sensor values under different driving working conditions in a manual driving mode are used as input vectors of a neural networkX＝(x i,j ) Normalized indexes of accelerator opening sensor values, brake pressure sensor values, gear information and steering wheel rotation angle sensor values under different driving working conditions in an unmanned mode are used as target output vectors Y= (Y) i ) The hidden layer takes 6 neurons, transfer functions of the hidden layer and the output layer are respectively selected as Sigmoid and Pureline functions, the training times are not less than 1000 times, the function relation between an input vector and an output vector is obtained, and the optimal behavior parameters under different running working conditions are obtained through the function relation;
step three: performing data fusion on the acquired optimal behavior parameter data and execution feedback information to generate a decision instruction with human driving characteristics;
step four: the environment sensing module acquires and processes decision influencing parameters in the driving process, the acquired decision influencing parameters are sent to the central control module through the CAN bus, the central control module generates a decision instruction with human driving characteristics according to the acquired decision influencing parameters, the decision instruction is sent to the transverse control module and the longitudinal control module, the transverse control module and the longitudinal control module send execution instructions to the decision execution module, the throttle opening degree is controlled through a throttle control mechanism of the decision execution module, the braking pressure is controlled through a braking control mechanism, the gear is controlled through a gear control mechanism, the steering wheel corner is controlled through a direction control mechanism, and execution feedback information of the decision execution module is collected and fed back to the central control module.
In the manual driving mode, the behavior data acquisition module acquires the values of the accelerator opening sensor, the brake pressure sensor, the gear information acquisition device, the steering wheel angle sensor and the like in the driving process, sends the values of the accelerator opening sensor, the brake pressure sensor, the gear information and the steering wheel angle sensor to the central control module for storage through the singlechip, extracts effective data after processing to serve as input vector values of the artificial neural network model, further perfects the prediction accurate values of the artificial neural network model, and serves as a feedback link in the closed-loop control of the control system in the automatic driving mode, and monitors the running conditions of the branch mechanisms such as the accelerator control mechanism, the brake control mechanism, the gear control mechanism and the steering wheel angle sensor of the decision execution module by acquiring the values of the accelerator opening sensor, the brake pressure sensor, the gear information, the steering wheel angle sensor and the like and sending the data to the central control module.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The utility model provides a control system of unmanned mode of car, includes environment perception module, horizontal control module, vertical control module, central control module, action data acquisition module and decision execution module, the environment perception module acquires and handles the decision influence parameter in the course of traveling to with the decision influence parameter of acquireing is passed through the CAN bus and is sent to central control module, central control module generates decision command according to the decision influence parameter of acquireing, and with decision command send to horizontal control module with vertical control module, by horizontal control module with vertical control module sends the execution command to decision execution module, and gathers the execution feedback information feedback of decision execution module arrives central control module realizes unmanned mode's closed loop control, its characterized in that: the behavior data acquisition module is connected with the central control module and the decision execution module through a CAN bus, receives characteristic information of the decision execution module in a manual driving mode, acquires optimal behavior parameters under different driving working conditions by constructing an artificial neural network model, and performs data fusion on the acquired optimal behavior parameters and execution feedback information to generate a decision instruction with human driving characteristics.
2. The control system for the unmanned mode of an automobile of claim 1, wherein: the environment sensing module comprises a camera, a laser radar, a millimeter wave radar and an INS/GPS integrated navigation system.
3. The control system for the unmanned mode of an automobile of claim 1, wherein: the decision execution module comprises an accelerator control mechanism, a brake control mechanism, a gear control mechanism and a direction control mechanism.
4. The control system for the unmanned mode of an automobile of claim 1, wherein: the behavior data acquisition module comprises a singlechip, an accelerator opening sensor, a brake pressure sensor, a gear information acquisition device and a steering wheel angle sensor.
5. A control system for an unmanned vehicle mode according to claim 2, wherein: the camera is distributed at the roof or the vehicle windshield and used for acquiring the information of a lane central line, traffic signals and traffic signs, the laser radars are uniformly distributed around the vehicle body and used for acquiring the distance, azimuth and profile data of static obstacles, the millimeter wave radars are fixedly arranged at the middle position of the top of the vehicle and used for acquiring the ID, azimuth, distance and speed of dynamic obstacles around the vehicle, and the INS/GPS integrated navigation system is used for acquiring the current longitude, latitude, course angle, speed, pitch angle, roll angle and GPS time information of the vehicle.
6. The control system for the unmanned mode of an automobile of claim 1, wherein: the characteristic information is an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving working conditions in an artificial driving mode.
7. The control system for the unmanned mode of an automobile of claim 1, wherein: the driving working conditions comprise a following working condition, a cruising working condition, a lane changing working condition and an obstacle avoidance working condition.
8. The control system for the unmanned mode of an automobile of claim 1, wherein: the execution feedback information is an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving working conditions in an unmanned mode.
9. The control system for the unmanned mode of an automobile of claim 1, wherein: the artificial neural network model takes characteristic information normalization indexes under different driving working conditions in an artificial driving mode as an input vector X= (X) of the neural network i,j ) Taking the normalization index of the execution feedback information under different driving conditions in the unmanned mode as a target output vector Y= (Y) i ) The hidden layer takes 6 neurons, transfer functions of the hidden layer and the output layer are respectively selected as Sigmoid and Pureline functions, and the training times are not less than 1000 times.
10. A control system for an unmanned vehicle mode according to any of claims 1 to 9, wherein: the control method of the control system comprises the following steps:
step one: acquiring an accelerator opening sensor value, a brake pressure sensor value, gear information and a steering wheel angle sensor value under different driving conditions in a manual driving mode and an unmanned driving mode respectively;
step two: normalized indexes of accelerator opening sensor values, brake pressure sensor values, gear information and steering wheel rotation angle sensor values under different driving working conditions in a manual driving mode are used as input vectors X= (X) of a neural network i,j ) Throttle opening sensor value, brake pressure sensor value, gear information and method under different driving working conditions in unmanned modeNormalized index of the steering wheel angle sensor value as target output vector y= (Y) i ) The hidden layer takes 6 neurons, transfer functions of the hidden layer and the output layer are respectively selected as Sigmoid and Pureline functions, the training times are not less than 1000 times, the function relation between an input vector and an output vector is obtained, and the optimal behavior parameters under different running working conditions are obtained through the function relation;
step three: performing data fusion on the acquired optimal behavior parameter data and execution feedback information to generate a decision instruction with human driving characteristics;
step four: the environment sensing module acquires and processes decision influencing parameters in the driving process, the acquired decision influencing parameters are sent to the central control module through a CAN bus, the central control module generates a decision instruction with human driving characteristics according to the acquired decision influencing parameters, the decision instruction is sent to the transverse control module and the longitudinal control module, the transverse control module and the longitudinal control module send execution instructions to the decision execution module, the throttle control mechanism of the decision execution module controls the opening degree of an accelerator, the brake control mechanism controls the brake pressure, the gear control mechanism controls the gear, the steering wheel corner is controlled through the direction control mechanism, and the execution feedback information of the decision execution module is collected and fed back to the central control module.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116238544A (en) * | 2023-05-12 | 2023-06-09 | 禾多科技（北京）有限公司 | Running control method and system for automatic driving vehicle |
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| CN116238544A (en) * | 2023-05-12 | 2023-06-09 | 禾多科技（北京）有限公司 | Running control method and system for automatic driving vehicle |
| CN116238544B (en) * | 2023-05-12 | 2023-07-14 | 禾多科技（北京）有限公司 | Running control method and system for automatic driving vehicle |
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