CN113386791A - Danger avoiding system based on unmanned transport vehicle train in heavy fog weather - Google Patents
Danger avoiding system based on unmanned transport vehicle train in heavy fog weather Download PDFInfo
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Abstract
The invention provides a danger avoiding system based on an unmanned transport vehicle train in foggy weather, which belongs to the field of unmanned vehicle train safe transport, and a visibility identification module comprises: the calculation unit is used for receiving the data information sent by the vehicle-road cooperative system and performing calculation processing on the data information; the grading module is used for finishing the classification of the road fog region grade and the determination of the operation mode according to the calculation result of the calculation unit; unmanned train control module includes: the vehicle-mounted OS is used for receiving signals sent by the vehicle-road cooperative system; the BCM vehicle body control module controls the corresponding controller according to the received information; the laser radar is in communication connection with the BCM vehicle body control module and is used for judging the current distance between the unmanned train and controlling the distance; and the vehicle-mounted ECU is used for controlling the current speed and finishing the speed and vehicle distance control. The system adopts the cooperation of the vehicle and the road and a deep learning algorithm to realize the whole-course monitoring of the visibility in the foggy weather and the vehicle operation control, and can greatly reduce traffic accidents.
Description
Technical Field
The invention belongs to the field of safe transportation of unmanned trains, and particularly relates to a danger avoiding system based on the unmanned trains in the foggy weather.
Background
With the continuous maturity of unmanned vehicle technology, unmanned vehicles are coming to the outbreak of the industry all over the world. Particularly, in the past two years, the logistics unmanned technology develops and matures gradually from the concept of a laboratory and is applied, and unmanned warehouses, unmanned planes and unmanned transport vehicles enter the public view in a dispute. Unmanned vehicles are the main research object of intelligent transportation systems, and autonomous fleet systems are important subsystems of the systems, wherein queues are the most common formation in the autonomous fleet systems.
The driving of the queue focuses on sensing and controlling the environment, although the traditional unmanned driving technology can well identify a route, avoid obstacles and the like, and can efficiently avoid traffic accidents caused by human vision blind areas, the development of the traditional unmanned driving technology also has many technical problems, such as severe environments such as foggy days, snow accumulation and the like, the sensing of the traditional unmanned driving technology to the environment is prone to deviation, and the weather environment, the road condition and the like are likely to cause accidents. In the existing identification system, the detection capability of the laser radar is greatly attenuated under the non-clear conditions such as dense fog, once extreme weather conditions such as dense fog occur, the excellent laser radar, sensor or camera cannot play a role easily, and the price of the laser radar is high. Moreover, the huge information flow requires a specialized processor to achieve the goal.
Based on the problems, the invention adopts a plurality of technologies to realize the control of the unmanned transport vehicle train in the foggy weather in an auxiliary way.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a danger avoiding system based on an unmanned transport vehicle train in the foggy weather, the system can classify the current fog degree by using a self-adaptive hybrid convolutional neural network, the result is sent to a queue through dsrc, the distance between vehicles is controlled through a laser radar after the queue receives the result, and the speed of the vehicle is controlled through vehicle-mounted ecu, so that the emergency danger avoiding of the unmanned transport vehicle train in the foggy weather is realized.
In order to achieve the above purpose, the invention provides the following technical scheme:
a danger avoiding system based on an unmanned transport vehicle train in foggy weather comprises a visibility identification module and an unmanned vehicle train control module;
the visibility recognition module includes:
the calculation unit is used for receiving the data information sent by the vehicle-road cooperation system and performing calculation processing on the data information;
the grading module is used for finishing the classification of the road fog region grade and the determination of the running mode according to the calculation result of the calculation unit and sending the road fog region grade and the running mode information to the vehicle-road cooperative system;
the unmanned train control module comprises;
the vehicle-mounted OS is used for receiving signals sent by the vehicle-road cooperative system;
the BCM vehicle body control module is used for receiving the road fog region grade and the operation mode information and controlling a corresponding controller according to the received information;
the laser radar is in communication connection with the BCM vehicle body control module and is used for judging the distance between the current unmanned train and controlling the distance;
and the vehicle-mounted ECU is in communication connection with the BCM vehicle body control module and is used for controlling the current vehicle speed.
Preferably, the vehicle-road coordination system comprises;
the drive test unit is used for acquiring the surrounding environment data of the current unmanned train;
the data transmitter is used for transmitting the current surrounding environment data of the unmanned train to the computing unit;
and the wireless network transmission module is used for connecting the grading module and the vehicle-mounted OS with the drive test unit through a wireless network to realize data transmission.
Preferably, the grading module utilizes an adaptive hybrid convolutional neural network algorithm to complete the classification of the road fog region grades and the determination of the operation mode.
Preferably, the wireless network transmission module uses an MK5 device for data transmission.
Preferably, the operation modes are divided according to visibility in a fog area, and specifically include the following:
when the visibility is 500-1000 m, the vehicle belongs to a 'low risk mode', the rear fog lamp is required to be started, the speed per hour does not exceed 80km/h, and the distance between vehicles is kept more than 150;
when the visibility is 200-500 m, the vehicle belongs to an 'intermediate risk mode', the rear fog lamp is required to be turned on, the speed per hour is not more than 70km/h, and the vehicle distance of more than 100m is kept;
when the visibility is 50-200 m, the high-risk mode is adopted, the front fog lamp and the rear fog lamp are required to be started, the speed per hour is not more than 60km/h, and the distance of more than 50m is kept;
when the visibility is below 50m, the vehicle belongs to a dangerous mode, the front fog lamp and the rear fog lamp are turned on and drive nearby to a fog area, and the speed per hour does not exceed 40 km/h.
The danger avoiding system based on the unmanned transport vehicle train in the foggy weather has the following beneficial effects:
(1) the invention, following the development trend of unmanned vehicles, breakthroughs the use of foggy roads as entry points, converts the identification information of road environment into an operation mode, sends the operation mode to the vehicle-mounted ECU, implements the control of the unmanned vehicles, and can solve the problem that the unmanned vehicle train in the current market is difficult to identify in foggy weather.
(2) Based on the unmanned train control system, aiming at the special condition of laser radar scattering attenuation in foggy weather, the fog environment and deep learning are analyzed through the vehicle-road cooperative system, the visibility is quickly judged from the foggy image, the grade classification is carried out, and the safety system of the unmanned train is promoted to a new grade.
(3) After the front vehicle receives the low visibility early warning, the subsequent train is quickly in a degraded running mode, the vehicle speed of the whole queue is reduced and the distance between the vehicles is planned again through the control of the vehicle-mounted ECU, and the queue is adjusted to be in a high risk mode.
(4) The deep learning algorithm provided by the self-adaptive hybrid convolutional neural network, the information transmission by using the wireless network transmission module, the control of the laser radar on the distance between vehicles and the like are greatly broken through in the aspect of improving the control efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention and the design thereof, the drawings required for the embodiments will be briefly described below. The drawings in the following description are only some embodiments of the invention and it will be clear to a person skilled in the art that other drawings can be derived from them without inventive effort.
FIG. 1 is a flow chart of how to implement the control of the speed and the distance of an unmanned transport vehicle on a highway in heavy fog weather according to an embodiment of the invention;
FIG. 2 is a schematic structural diagram of how to implement the control of the speed and the distance of an unmanned transport vehicle on a highway in heavy fog weather according to the embodiment of the invention;
FIG. 3 is a schematic diagram of data acquisition, reception and headway control for an unmanned transport vehicle train in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of the location of vehicle control units and systems within an unmanned train in accordance with an embodiment of the present invention;
FIG. 5 is a diagram illustrating a visibility recognition module based on an improved full convolution neural network in an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention and can practice the same, the present invention will be described in detail with reference to the accompanying drawings and specific examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
Aiming at the problems of function attenuation of unmanned vehicle laser radar in a fog environment and low information transmission efficiency of a vehicle-mounted sensor, the invention provides a danger avoiding system based on an unmanned transport vehicle train in a foggy weather, provides a grading early warning concept, and enables the unmanned vehicle train to finish following tasks in different visibility conditions more efficiently and accurately in response to different modes. The problem that the unmanned vehicle is easy to have accidents under the condition of low visibility is solved, and the method has important significance for the development of the field of unmanned transportation safety. As shown in fig. 1 to 4, the system comprises a visibility recognition module and an unmanned train control module;
the visibility identification module comprises a calculation unit and a grading module;
the calculation unit is used for receiving the data information sent by the vehicle-road cooperative system and performing calculation processing on the data information; the grading module is used for finishing the classification of the road fog region grade and the determination of the operation mode according to the calculation result of the calculation unit and sending the road fog region grade and the operation mode information to the vehicle-road cooperative system;
the unmanned train control module comprises a vehicle-mounted OS, a BCM vehicle body control module, a laser radar and a vehicle-mounted ECU;
the vehicle-mounted OS is used for receiving signals sent by the vehicle-road cooperative system; the BCM vehicle body control module is used for receiving road fog region grade and operation mode information and controlling a corresponding controller according to the received information; the laser radar is in communication connection with the BCM vehicle body control module and is used for judging the current distance between the unmanned train and controlling the distance; the vehicle-mounted ECU is in communication connection with the BCM vehicle body control module, and a vehicle-mounted receiving unit, a vehicle speed control unit, a vehicle head control unit, a spacing control unit and the like are arranged in the vehicle-mounted ECU and used for controlling the current vehicle speed and finishing speed and vehicle spacing control. The vehicle speed is controlled by sending a control command to an execution pointer through a transmitter and a signal generator, and execution components comprise a steering system, a braking system and the like.
Specifically, in this embodiment, the vehicle-road cooperation system includes a drive test unit, a data transmitter, and a wireless network transmission module;
the drive test unit is used for acquiring the surrounding environment data of the current unmanned train; the data transmitter is used for transmitting the current surrounding environment data of the unmanned train to the computing unit; and the wireless network transmission module is used for connecting the grading module and the vehicle-mounted OS with the drive test unit through a wireless network to realize data transmission. Specifically, the wireless network transmission module of this embodiment uses an MK5 device to perform data transmission. The MK5 equipment is a mature and widely applicable Internet of vehicles product, and can have super-strong wireless communication capability in severe environments such as the foggy weather studied by the system. And mapping the change of the surrounding environment of the train into the driving state in real time to form a real-time information fusion public data pool. And the driving environment information data pool is used as a basis to help the train to implement decision making.
In this embodiment, the classification module uses a adaptive hybrid Neural Network (CNN) algorithm to complete the classification of the road fog zone level and the determination of the operation mode. In order to fully utilize video monitoring along the highway and realize whole-process monitoring of visibility in the foggy weather of the highway. As shown in fig. 5, a ResNet and VGG19 pre-training model is used for transfer learning in a CNN algorithm input layer to prevent the over-fitting phenomenon from occurring in the training result; and a parameter self-adaptive adjusting module is constructed between the forward and backward propagation processes of the algorithm, and the weight self-adaptive updating is realized according to the training period number of the convolutional neural network and the training error enhancement parameter coefficient, so that the convergence speed and the visibility identification accuracy of model training are effectively improved.
Specifically, the operation modes of the embodiment are divided according to the visibility of the fog area, and specifically include the following:
when the visibility is 500-1000 m, the vehicle belongs to a 'low risk mode', the rear fog lamp is required to be started, the speed per hour does not exceed 80km/h, and the distance between vehicles is kept more than 150;
when the visibility is 200-500 m, the vehicle belongs to an 'intermediate risk mode', the rear fog lamp is required to be turned on, the speed per hour is not more than 70km/h, and the vehicle distance of more than 100m is kept;
when the visibility is 50-200 m, the high-risk mode is adopted, the front fog lamp and the rear fog lamp are required to be started, the speed per hour is not more than 60km/h, and the distance of more than 50m is kept;
when the visibility is below 50m, the vehicle is in a dangerous mode, the front fog light and the rear fog light are turned on and drive nearby to a fog area, and the speed per hour does not exceed 40km/h
The working principle of the risk avoiding system provided in the embodiment is as follows:
firstly, an information collection link is adopted, a road condition is collected by a drive test unit through a microwave transceiving mode, and a data transmitter can transmit information to a computing unit through wireless communication. And secondly, an information processing link, wherein the computing unit can process the received road information through a self-adaptive hybrid convolutional neural network algorithm to finish the grading of the fog. Finally, a train control link is carried out, the fog grade information is transmitted to the unmanned train through a wireless communication network, and early warning is carried out; and the vehicle-mounted OS and BCM vehicle body control module determines the current driving mode according to the fog level, so that the driving speed and the inter-vehicle distance are controlled in the range corresponding to the current mode.
In practical application, the RSU drive test unit is laid on a corresponding road surface, the road condition is collected in a microwave receiving and transmitting mode, and short-range communication is carried out between the RSU drive test unit and the OBU vehicle-mounted unit. The OBU is equivalent to a signal receiver and can realize information interaction among a plurality of systems. The data collected by the drive test unit can classify the current fog degree through a deep learning algorithm, and information is transmitted to the vehicle-mounted ECU according to types, so that the speed control of the unmanned train is realized. The deep learning algorithm uses an adaptive hybrid convolutional neural network to classify the degree of image processing.
When the unmanned train passes through a dangerous road section, the judgment of the running speed, the distance between vehicles and the like is very important. For example, in a road section where fog is easily generated: g large-wide high-speed, G65 best-packed high-speed, G60 Hukun high-speed and G70 Fuyin high-speed. The invention can provide powerful guarantee for controlling the speed and the distance of the unmanned train at the heavy fog road section, and how to utilize the cooperative system of the train and the road to collect the road information, judge the heavy fog degree and control the unmanned train are the key contents of the invention and are an important ring for improving the traffic safety.
The danger avoiding system provided by the embodiment aims at the unmanned transport vehicle. For the vehicle-mounted ECU installed on the vehicle, the important function is to determine the proper vehicle speed and vehicle distance according to the obtained visibility grade so as to reduce the collision and the damage of transported goods in the vehicle caused by sudden braking, and the effective utilization of the system on the transport vehicle can effectively reduce the probability of accidents of the unmanned transport vehicle and improve the transport efficiency of the transport vehicle.
The above-mentioned embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any simple modifications or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (5)
1. A danger avoiding system based on an unmanned transport vehicle train in foggy weather is characterized by comprising a visibility identification module and an unmanned vehicle train control module;
the visibility recognition module includes:
the calculation unit is used for receiving the data information sent by the vehicle-road cooperation system and performing calculation processing on the data information;
the grading module is used for finishing the classification of the road fog region grade and the determination of the running mode according to the calculation result of the calculation unit and sending the road fog region grade and the running mode information to the vehicle-road cooperative system;
the unmanned train control module comprises;
the vehicle-mounted OS is used for receiving signals sent by the vehicle-road cooperative system;
the BCM vehicle body control module is used for receiving the road fog region grade and the operation mode information and controlling a corresponding controller according to the received information;
the laser radar is in communication connection with the BCM vehicle body control module and is used for judging the distance between the current unmanned train and controlling the distance;
and the vehicle-mounted ECU is in communication connection with the BCM vehicle body control module and is used for controlling the current vehicle speed.
2. The danger avoiding system based on the unmanned transport vehicle train in the foggy weather as claimed in claim 2, wherein the vehicle path coordination system comprises;
the drive test unit is used for acquiring the surrounding environment data of the current unmanned train;
the data transmitter is used for transmitting the current surrounding environment data of the unmanned train to the computing unit;
and the wireless network transmission module is used for connecting the grading module and the vehicle-mounted OS with the drive test unit through a wireless network to realize data transmission.
3. The risk avoiding system based on the unmanned transport vehicle train in the foggy weather as claimed in claim 1, wherein the grading module utilizes an adaptive hybrid convolutional neural network algorithm to complete the grading of the road fog area and the determination of the operation mode.
4. The danger avoiding system based on the unmanned transport vehicle train in the foggy weather as claimed in claim 1, wherein the wireless network transmission module adopts an MK5 device for data transmission.
5. The danger avoiding system based on the unmanned transport vehicle train in the foggy weather as claimed in claim 1, wherein the operation modes are divided according to visibility in a foggy area, and specifically include the following:
when the visibility is 500-1000 m, the vehicle belongs to a 'low risk mode', the rear fog lamp is required to be started, the speed per hour does not exceed 80km/h, and the distance between vehicles is kept more than 150;
when the visibility is 200-500 m, the vehicle belongs to an 'intermediate risk mode', the rear fog lamp is required to be turned on, the speed per hour is not more than 70km/h, and the vehicle distance of more than 100m is kept;
when the visibility is 50-200 m, the high-risk mode is adopted, the front fog lamp and the rear fog lamp are required to be started, the speed per hour is not more than 60km/h, and the distance of more than 50m is kept;
when the visibility is below 50m, the vehicle belongs to a dangerous mode, the front fog lamp and the rear fog lamp are turned on and drive nearby to a fog area, and the speed per hour does not exceed 40 km/h.
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| CN111882904A (en) * | 2020-07-27 | 2020-11-03 | 扬州大学 | Port area environment unmanned heavy truck safety early warning method based on edge calculation |
| CN112863201A (en) * | 2021-01-07 | 2021-05-28 | 武汉理工大学 | Guiding method based on expressway agglomerate fog area multi-stage early warning system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115631656A (en) * | 2022-12-20 | 2023-01-20 | 北京卓翼智能科技有限公司 | Control system of unmanned vehicle and unmanned vehicle thereof |
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