CN111935250B - Automatic driving data classification transmission method and system - Google Patents
Automatic driving data classification transmission method and system Download PDFInfo
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- CN111935250B CN111935250B CN202010725482.2A CN202010725482A CN111935250B CN 111935250 B CN111935250 B CN 111935250B CN 202010725482 A CN202010725482 A CN 202010725482A CN 111935250 B CN111935250 B CN 111935250B
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H—ELECTRICITY
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Abstract
The invention discloses a classified transmission method and a classified transmission system for automatic driving data, which belong to the technical field of automatic driving and comprise the following steps: acquiring data acquired by each sensor in the automatic driving process as to-be-transmitted data; classifying and packaging the data to be transmitted according to the message attribute of the data to be transmitted to obtain a classified data packet; and carrying out coding transmission on the classified data packet. The invention can make the diversified mass data generated in the automatic driving process clearly classified and not disordered any more, and ensures the accuracy of data transmission.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a classified transmission method and system for automatic driving data.
Background
With the continuous upgrading of the automatic driving technology, artificial intelligence has deepened into various industries, the industry and the field applying the automatic driving technology at present have a blowout trend, for example, along with the increase of electric automobiles, the automatic charging industry needs the automatic driving technology of the charging vehicle, for example, when facing to a novel coronavirus epidemic situation, the automatic driving delivery vehicle is needed more, and the like. In the face of such continuously expanding automatic driving application fields, how to efficiently encode and transmit mass data of various sensors is taken as a key basis for supporting an automatic driving technology, and becomes a key index for safety, reliability and stable performance of the automatic driving technology.
At present, a data classification transmission mechanism is generally a time classification mechanism, that is, data is classified according to the acquisition time of the data, and the data is sorted and combined according to a period of every day or every week and then stored. Although the time classification mechanism can help achieve the purpose of data classification, the disadvantages are obvious, namely data chaos: data classification is performed every day or every week, and data in the period is uncontrollable, namely the data types are various, and it is very difficult to obtain needed data information from the period in the data confusion period. Secondly, the error rate is high: because the data are classified according to the data acquisition time, if the data acquisition time is abnormal due to abnormality in the acquisition process, the data are wrongly classified according to a time classification mechanism.
Because the automatic driving requires a real-time processing mechanism in principle, how to save time and efficiently and accurately encode and decode data in the process of classifying and transmitting mass data is a core technology for improving the automatic driving technology.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, so that the automatic driving data is accurately classified and the data category is clear.
In order to achieve the above purpose, the present invention adopts an automatic driving data classification transmission method, which comprises the following steps:
acquiring data acquired by each sensor in the automatic driving process as data to be transmitted;
classifying and packaging the data to be transmitted according to the message attribute to which the data to be transmitted belongs to obtain a classified data packet;
and carrying out coding transmission on the classified data packet.
Further, the classifying and packaging the data to be transmitted according to the message attribute to which the data to be transmitted belongs to obtain a classified data packet, including:
dividing the data to be transmitted under the corresponding message attribute into an array according to the message attribute to which the data to be transmitted belongs, and taking the Fields [ ] array under each message attribute as classified data;
packaging the classified data to obtain a classified data packet Message; wherein the Message comprises a Message attribute and a Fields [ ] array, the Message attribute comprising at least one of an autonomous driving scenario SIN, an autonomous driving device performance indicator PIN, and a Message field category MIN.
Further, the Fields [ ] array contains the data field Name, the Value, the Element, and another Message nested, the Element containing the Index number, index, and another Fields [ ] array nested.
Further, the encoding transmission of the classified data packet includes:
encoding the classified data packet to obtain encoded data;
and encrypting the encoded data and transmitting the encrypted data.
Further, before the encoding and transmitting the classified data packet, the method further includes:
and dynamically adjusting the performance index PIN of the automatic driving equipment.
On the other hand, adopt an automatic driving data classification transmission system, including automatic driving equipment end and cloud platform end, carry out the information interaction through the HTTP agreement between automatic driving equipment end and the cloud platform end, automatic driving equipment end and cloud platform end all can regard as data transmission end and data receiving terminal, when automatic driving equipment end is as data transmission end, including data acquisition module, data classification packing module, coding module and transmission module, wherein:
the data acquisition module is used for acquiring data acquired by each sensor in the automatic driving process as to-be-transmitted data;
the data classification and packaging module is used for classifying and packaging the data to be transmitted according to the message attribute to which the data to be transmitted belongs to obtain a classification data packet;
the encoding module is used for encoding the classified data packet;
the transmission module is used for transmitting the encoded data to a data receiving end.
Further, the data classification and packaging module comprises a data classification unit and a data packaging unit, wherein:
the data classification unit is used for dividing the data to be transmitted under the corresponding message attribute into an array according to the message attribute to which the data to be transmitted belongs, and taking the Fields [ ] array under each message attribute as classified data;
the data packing unit is used for packing the classified data to obtain the classified data packet Message; wherein the Message comprises a Message attribute and a Fields [ ] array, the Message attribute comprising at least one of an autonomous driving scenario SIN, an autonomous driving device performance indicator PIN, and a Message field category MIN.
Further, the Fields [ ] array contains the data field Name, the Value, the Element, and another Message nested, the Element containing the Index number, index, and another Fields [ ] array nested.
Further, the cloud platform terminal is used as a data receiving terminal and comprises a data receiving module, a decoding module, a data classification module and a data storage module, wherein:
the data receiving module is used for receiving the coded data sent by the current data sending end;
the decoding module is used for decoding the coded data to obtain decoded data;
the data classification module is used for carrying out data classification on the message attribute and the Fields [ ] array in the decoded data packet;
and the data storage module is used for storing the classified data.
Further, the data classification module comprises a message attribute extraction unit, a message attribute judgment unit and a matching unit, wherein:
the message attribute extraction unit is used for extracting the message attributes in the decoded data packet;
the message attribute judging unit is used for judging the extracted message attribute type;
the matching unit is used for matching the data in the Fields [ ] array according to the obtained message attribute types.
Compared with the prior art, the invention has the following technical effects: the invention divides the collected data into different message attributes by combining the data characteristics collected by various sensors in the automatic driving process, divides the data under the corresponding message attributes into data groups according to the message attributes to which the collection belongs, packs the data groups under the message attributes into classified data packets, and performs coding transmission. And the message attributes and the corresponding data types are digitally represented in the transmission process, the data types are clear, diversified mass data generated in the automatic driving process are not disordered any more, and the accuracy of data transmission is ensured.
Drawings
The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
FIG. 1 is a flow chart of a method of automated driving data classification transmission according to the present invention;
FIG. 2 is a schematic diagram of the data sorting mechanism of the present invention;
FIG. 3 is a block diagram example of message attributes in the present invention;
FIG. 4 is another structural example of message attributes in the present invention;
fig. 5 is a block diagram of an automatic driving data classification transmission system according to the present invention.
Detailed Description
To further illustrate the features of the present invention, please refer to the detailed description and accompanying drawings below. The drawings are for reference and illustration purposes only and are not intended to limit the scope of the present disclosure.
As shown in fig. 1, the present embodiment discloses an automatic driving data classification transmission method, which includes the following steps S1 to S2:
s1, acquiring data acquired by each sensor in an automatic driving process as to-be-transmitted data;
it should be noted that, in this embodiment, data collected by various sensors carried in the automatic driving process is used as data to be transmitted, and the data includes environment sensing data, positioning data, speed data, path planning data, and the like.
S2, classifying and packaging the data to be transmitted according to the message attribute of the data to be transmitted to obtain a classified data packet;
it should be noted that, in this embodiment, multiple message attributes are set in combination with the type of data acquired in the automatic driving process, and the data to be transmitted is divided according to the message attributes to which the data belongs, so that the data category is clear compared with the conventional data classification according to a time classification mechanism.
And S3, coding and transmitting the classified data packet.
Further, the step S2: classifying and packaging the data to be transmitted according to the message attribute of the data to be transmitted to obtain a classified data packet, wherein the classified data packet comprises the following subdivision steps S21 to S22:
s21, dividing the data to be transmitted under the corresponding message attribute as a type into an array according to the message attribute to which the data to be transmitted belongs, and taking the Fields [ ] array under each message attribute as classified data;
s22, packaging the classification data to obtain a classification data packet Message; wherein the Message comprises a Message attribute and a Fields [ ] array, the Message attribute comprising at least one of an autonomous driving scenario SIN, an autonomous driving device performance indicator PIN, and a Message field category MIN.
Wherein the autopilot scenario SIN may include: SIN1: expressway, SIN2: urban roads, SIN3 closed areas and the like. The autopilot device performance index PIN may include: PIN1: the device collects data cycle, PIN2: device time synchronization interval, PIN3: the last data sending time of the device, PIN4: device network status, etc. The message field categories MIN may include: MIN1: acceleration and deceleration data, MIN2: time data, MIN3: latitude and longitude data, MIN4: altitude data, MIN5: speed data, etc.
The structure of the classified data packet Message is shown in fig. 2, the Fields [ ] array includes the data field names of the various sensors, the Value, the Element and another nested Message, and the Element includes the Index number Index and another nested Fields [ ] array.
The Fields array in this embodiment may be nested with another, and the data type in the nested Fields array may be the same as or different from the data type in the previous Fields array.
It should be noted that, in the conventional technology, the device side sends 2 packets of data to the platform side, and needs to obtain the first packet of data and then send the first packet of data to the platform side, and then obtains the second packet of data and sends the second packet of data to the platform side. In the embodiment, the Fields are nested in other Fields, that is, the Fields of the second packet are nested in the Fields of the first packet for transmission, so that the problems of data loss and the like caused by an excessively high transmission rate are solved. Compared with the condition that the traffic is excessively wasted by sending a single packet once, more traffic can be saved in the presence of mass data.
It should be noted that the field [ ] array structure shown in fig. 2 is only an example, and the field [ ] array structure may also be composed of five sets of attribute units, i.e., PIN, fromDeviceMessage, MIN, and todeviemessage.
The structure of the message attribute is shown in fig. 3 and fig. 4, in this embodiment, the message attribute includes a parent message attribute and a child message attribute, the parent message attribute is the upper level of the child message attribute, and the parent message attribute includes multiple child message attributes. In this embodiment, SIN, the autopilot PIN, and the MIN may be used as the parent message attribute or the child message attribute. For example, SIN may have a parent message attribute, and SIN may have a child message attribute PIN and a child message attribute MIN, for example, PIN may have a parent message attribute, and PIN may have a child message attribute SIN and a child message attribute MIN.
It should be noted that the message attribute in this embodiment specifies the data type, that is, the data type can be clearly known according to SIN, MIN, and PIN, which is more beneficial to data classification. The subclass message attribute may further include FromDeviceMessage and TodeviceMessage, and the data type is consistent with the normal data type. FromDeviceMessage indicates that data originates from the device side, todeviceMessage indicates that data is sent to the device, and data originates from the platform side. By setting the two message attributes, the data sources can be distinguished, and data confusion is avoided.
The following classification packet format obtained by encapsulating the data classification mechanism in this embodiment is:
{
"NAME" means "latitude and longitude data",
“SIN”:1,
“PIN”:1,
“MIN”:1,
“CREATED_AT”:2020-03-24 15:09,
“SEND_AT”:2020-03-24 15:11,
“Fields”:[
{
"name": first packet ",
“latitude”:31.22280,
“longitude”:121.54646,
“captured_at”:”2020-03-24 15:09”,
}，
{
"name" means "second package",
“latitude”:31.22281,
“longitude”:121.54655,
“captured_at”:”2020-03-24 15:10”,
}
]
}
wherein:
"NAME" is the longitude and latitude data: indicating the classified packet name, which is generally consistent with the data type.
And SIN1 represents that the automatic driving scene is a highway.
"MIN":1 represents latitude and longitude data.
"PIN" 1 means that latitude and longitude data is acquired for 1 minute.
"create _ AT": 2020-03-24-15: 09: represents the creation time of this packet (note: generally consistent with the data collection time of the first packet in the Fields [ ] array).
"SEND _ AT": 2020-03-24: 11: represents the transmission time of the data packet to the cloud platform.
"Fields" [ ] are expressed as latitude and longitude data arrays.
"name" first package "indicates the name of each package in the Fields array (remarks: the name of each package in the Fields array can be set at will, and generally accumulates with the number of times data is collected)
"latitude" of 31.22280 indicates that the collected dimensional data is 31.22280.
"Longitude" 121.54646 indicates that the collected longitude data is 121.54646.
"captured _ at": 2020-03-24-09 "indicates the data acquisition time for each packet in the Fields [ ] array.
Further, the step S3: the classified data packet is coded and transmitted, and the method comprises the following subdivision steps S31 to S32:
s31, coding the classified data packet to obtain coded data;
and S32, encrypting the encoded data and transmitting the encrypted data.
It should be noted that, in this embodiment, the UTF-8 is used to encode the classified data packet, and the MD5 encryption algorithm is used to encrypt the encoded data for transmission.
It should be understood that the encoding method and the encryption algorithm in this embodiment are only examples, and those skilled in the art may select other encoding methods and encryption algorithms to encode and encrypt the classified data according to actual situations.
Further, in this embodiment, each message attribute in the message attribute structure is represented digitally, and specific number numbers of each message attribute in advance are agreed at the data sending end and the data receiving end, for example, SIN1 represents a highway service area scene, SIN2 represents an apartment residential district scene, PIN1 represents a time period for a sensor to collect data, PIN2 represents a sensor early warning gate valve, MIN1 represents geographical latitude and longitude, MIN2 represents speed, and the like.
It should be noted that, in this embodiment, the SIN, the MIN, and the PIN are represented by using the number, so that the data attribute can be concise and clear, and data classification is facilitated.
Further, in this embodiment, before the encoding and transmitting the classified data packet, the method further includes: and dynamically adjusting the performance index PIN of the automatic driving equipment so as to realize dynamic adjustment of the data type in the Fields [ ] array corresponding to the PIN.
Sending a cycle example of acquiring latitude and longitude data by an equipment sending end, and dynamically adjusting data types in a corresponding Fields [ ] array:
1) The equipment end periodically collects longitude and latitude data;
2) The sending end adjusts the PIN value to PIN according to the collected longitude and latitude data: 1;
3) And encapsulating the acquired data into a data packet for sending.
The present embodiment dynamically adjusts the autopilot device performance index by numerical numbering according to the PIN. The data transmission rate can be improved: according to the dynamic adjustment of the PIN, the data sending rate of the data sending end is effectively improved. And (3) reducing the data chaos rate: according to the dynamic adjustment of the PIN, the performance index of each automatic driving device is determined, and the chaos rate of mass data is reduced.
Further, in this embodiment, different preprocessing operations are performed according to the data type in the Fields [ ] array corresponding to the MIN, specifically:
1) If the data type is acceleration and deceleration data, corresponding module algorithm filtering is carried out, and a Kalman filtering algorithm is adopted, so that the coding and decoding efficiency of the multivariate data is improved. The Kalman filtering algorithm is an algorithm which utilizes a linear system state equation, outputs an observation matrix through system input and output and performs optimal estimation on a system state according to variance.
The Kalman filtering algorithm is divided into a prediction and correction stage:
1-1) prediction phase:
and (3) a priori estimation:
1X(k|k-1)＝AX(k-1|k-1)+BU(k)
where A and B are matrix coefficients, X (k | k-1) is the result predicted from the previous state, X (k-1) is the last optimal result, and U (k) is the control quantity of the current state.
Error covariance:
2P(k|k-1)＝AP(k-1|k-1)AT+Q，
where P (k | k-1) is the covariance of X (k | k-1), P (k-1) is the covariance of X (k-1 non-zero k-1), and Q is the error covariance of the estimation process.
1-2) correction phase
Calculating a Kalman gain:
1Kg(k)＝P(k|k-1)HT/(HP(k|k-1)HT+R)
where H is also the coefficient matrix and R is the noise covariance of the measurements.
The correction estimate Z (K) is a measured value:
2X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1))。
updating the error covariance:
3P(k|k)＝(I-Kg(k)H)P(k|k-1)，
wherein, I is an identity matrix, and for a single model, the single measurement I is 1.
It should be noted that the filtering manner in this embodiment is divided into hardware filtering and software filtering, where the hardware filtering: and filtering by using an RC filter consisting of a resistor and a capacitor with certain specifications or an LC filter consisting of an inductor and a capacitor and other analog filters. Software filtering: also known as digital filtering, reduces or attenuates the effects of noise by certain algorithms or decision programs. In this embodiment, a Kalman (Kalman) filtering algorithm is selected, which is convenient for a computer to implement and can update and process the field data in real time. After the acceleration and deceleration data pass through a Kalman filtering algorithm, the data are uploaded to a cloud platform, so that the accuracy of the data is ensured.
2) If the data type is longitude and latitude data, the coordinates need to be converted into coordinates of a WGS84 (World geographic System 1984) according to a coordinate System conversion algorithm, so as to avoid coordinate offset. Examples are as follows:
2-1) the equipment data sending end sends the longitude and latitude data of the BD09 coordinate system to the platform data receiving end, and the platform data receiving end successfully receives the data and converts the data into coordinates of a WGS84 coordinate system.
2-2) the platform data sending end sends the longitude and latitude data of the WGS84 coordinate system to the equipment data receiving end, and the equipment data receiving end successfully receives the data and then converts the data into the BD09 coordinate system.
3) If the data is Time type data, the data needs to be converted into a Universal Time (UTC) Time zone of the world, so that the consistency of the Time zones is ensured, time errors caused by different Time zones are avoided, and the data is stored after the conversion is finished.
Further, in this embodiment, the step of processing the received classified data packet includes:
decrypting the received classified data packet, and decoding the decrypted data to obtain decoded data;
carrying out data classification on the message attribute and the Fields [ ] array in the decoded data;
and storing or applying the classified data.
It should be noted that, after the decoding is completed, the process of classifying the data is as follows:
1) Judging which automatic driving scene belongs to according to the SIN;
2) Judging the type of the automatic driving data according to the MIN;
3) And judging the automatic driving setting performance according to the PIN.
The decoding process in this embodiment can improve the speed of data classification: the data types represented by each attribute such as SIN, MIN and the like are definitely known, and the data are analyzed according to the types after being received, so that the speed of classifying mass data is greatly improved. And (3) reducing the data classification error rate: when the data classification mechanism is used for classifying data, the error rate of mass data classification is greatly reduced.
As shown in fig. 5, this embodiment discloses an automatic driving data classification transmission system, including automatic driving equipment end and cloud platform end, carry out the information interaction through the HTTP protocol between automatic driving equipment end and the cloud platform end, automatic driving equipment end and cloud platform end all can regard as data sending end and data receiving end, when automatic driving equipment end is as data sending end, when the cloud platform end is as the data receiving end:
1) The automatic driving equipment end comprises a data acquisition module, a data classification and packaging module, an encoding module and a transmission module, wherein:
the data acquisition module is used for acquiring data acquired by each sensor in the automatic driving process as data to be transmitted;
the data classification and packaging module is used for classifying and packaging the data to be transmitted according to the message attribute of the data to be transmitted to obtain a classification data packet;
the encoding module is used for encoding the classified data packet;
the transmission module is used for transmitting the encoded data to a data receiving end.
Wherein, the data classification packing module comprises a data classification unit and a data packing unit, wherein:
the data classification unit is used for dividing the data to be transmitted under the corresponding message attribute into an array according to the message attribute to which the data to be transmitted belongs, and taking the Fields [ ] array under each message attribute as classified data;
the data packing unit is used for packing the classified data to obtain the classified data packet Message; wherein the Message comprises a Message attribute and a Fields [ ] array, the Message attribute comprising at least one of an autonomous driving scenario SIN, an autonomous driving device performance indicator PIN, and a Message field category MIN.
The Fields [ ] array contains the data field Name, the Value, the Element, which contains the Index number Index and another Fields [ ] array of the nest, and another Message of the nest.
2) The cloud platform end is used as a data receiving end and comprises a data receiving module, a decoding module, a data classification module and a data storage module, wherein:
the data receiving module is used for receiving the coded data sent by the current data sending end;
the decoding module is used for decoding the coded data to obtain decoded data;
the data classification module is used for carrying out data classification on the message attribute and the Fields [ ] array in the decoded data packet;
and the data storage module is used for storing the classified data.
The data classification module comprises a message attribute extraction unit, a message attribute judgment unit and a matching unit, wherein:
the message attribute extraction unit is used for extracting the message attributes in the decoded data packet;
the message attribute judging unit is used for judging the extracted message attribute type;
the matching unit is used for matching the data in the Fields [ ] array according to the obtained message attribute types.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. An automatic driving data classification transmission method is characterized by comprising the following steps:
acquiring data acquired by each sensor in the automatic driving process as data to be transmitted;
classifying and packaging the data to be transmitted according to the message attribute to which the data to be transmitted belongs to obtain a classified data packet;
carrying out coding transmission on the classified data packet;
the classifying and packaging the data to be transmitted according to the message attribute to which the data to be transmitted belongs to obtain a classified data packet, which comprises the following steps:
dividing the data to be transmitted under the corresponding message attribute into an array according to the message attribute to which the data to be transmitted belongs, and taking the Fields [ ] array under each message attribute as classified data;
packaging the classified data to obtain a classified data packet Message; the Message comprises a Message attribute and a Fields [ ] array, wherein the Message attribute comprises at least one of an automatic driving scene SIN, an automatic driving equipment performance index PIN and a Message field type MIN;
the Fields [ ] array contains the data field Name, the Value, the Element containing the Index number Index and another Fields [ ] array nested, and another Message nested;
before the encoding transmission of the classified data packet, the method further comprises: dynamically adjusting the performance index PIN of the automatic driving equipment, specifically dynamically adjusting the performance index of the automatic driving equipment according to the number of the PIN;
and according to the data type in the Fields [ ] array corresponding to the MIN, performing different preprocessing operations, specifically:
1) If the data type is acceleration and deceleration data, filtering the corresponding module algorithm, and adopting a Kalman filtering algorithm which is divided into a prediction stage and a correction stage:
1-1) prediction phase:
and (3) a priori estimation:
1X(k|k-1)=AX(k-1|k-1)+BU(k)
wherein, A and B are matrix coefficients, X (k | k-1) is a result predicted according to a previous state, X (k-1) is an optimal result of the previous time, and U (k) is a control quantity of a current state;
error covariance:
2P(k|k-1)=AP(k-1|k-1)AT+Q，
wherein P (k | k-1) is the covariance of X (k | k-1), P (k-1 non-zero k-1) is the covariance of X (k-1 non-zero k-1), and Q is the error covariance of the estimation process;
1-2) correction phase
Calculating a Kalman gain:
1Kg(k)=P(k|k-1)HT/(HP(k|k-1)HT+R)
wherein H is also a coefficient matrix, and R is a noise covariance of the measured values;
the correction estimate Z (K) is a measured value:
2X(k|k)=X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1))；
updating the error covariance:
3P(k|k)=(I-Kg(k)H)P(k|k-1)，
wherein, I is an identity matrix, and for a single model, the single measurement I is 1;
2) If the data type is longitude and latitude data, converting the coordinate into a WGS84 coordinate system according to a coordinate system conversion algorithm to avoid coordinate offset;
3) If the data is time type data, converting the data into the same time zone in the world;
after the decoding is finished, the process of classifying the data is as follows:
judging which automatic driving scene belongs to according to the SIN;
judging the type of the automatic driving data according to the MIN;
and judging the automatic driving setting performance according to the PIN.
2. The automated driving data classification transmission method according to claim 1, wherein the encoding transmission of the classification data packet comprises:
coding the classified data packet to obtain coded data;
and encrypting the encoded data and transmitting the encrypted data.
3. An automatic driving data classified transmission system capable of realizing the automatic driving data classified transmission method according to claim 1 or 2, comprising an automatic driving device end and a cloud platform end, wherein the automatic driving device end and the cloud platform end perform information interaction through an HTTP protocol, the automatic driving device end and the cloud platform end can both serve as a data sending end and a data receiving end, and when the automatic driving device end serves as a data sending end, the automatic driving data classified transmission system comprises a data acquisition module, a data classified packing module, an encoding module and a transmission module, wherein:
the data acquisition module is used for acquiring data acquired by each sensor in the automatic driving process as data to be transmitted;
the data classification and packaging module is used for classifying and packaging the data to be transmitted according to the message attribute to which the data to be transmitted belongs to obtain a classification data packet;
the encoding module is used for encoding the classified data packet;
the transmission module is used for transmitting the encoded data to a data receiving end.
4. The automatic driving data classification transmission system according to claim 3, wherein the data classification packing module includes a data classification unit and a data packing unit, wherein:
the data classification unit is used for dividing the data to be transmitted under the corresponding message attribute into an array according to the message attribute to which the data to be transmitted belongs, and taking the Fields [ ] array under each message attribute as classified data;
the data packing unit is used for packing the classified data to obtain the classified data packet Message; wherein the Message comprises a Message attribute and a Fields [ ] array, the Message attribute comprising at least one of an autonomous driving scenario SIN, an autonomous driving equipment performance indicator PIN, and a Message field category MIN.
5. The automatic driving data classification transmission system of claim 4, wherein the Fields [ ] array contains a data field Name, a Value, an Element containing an Index number Index and another Fields [ ] array nested, and another Message nested.
6. The automatic driving data classification transmission system according to claim 3, wherein the cloud platform terminal is used as a data receiving terminal and comprises a data receiving module, a decoding module, a data classification module and a data storage module, wherein:
the data receiving module is used for receiving the coded data sent by the current data sending end;
the decoding module is used for decoding the coded data to obtain decoded data;
the data classification module is used for carrying out data classification on the message attribute and the Fields [ ] array in the decoded data packet;
the data storage module is used for storing the classified data.
7. The automatic driving data classification transmission system according to claim 6, wherein the data classification module includes a message attribute extraction unit, a message attribute judgment unit, and a matching unit, wherein:
the message attribute extraction unit is used for extracting the message attributes in the decoded data packet;
the message attribute judging unit is used for judging the extracted message attribute type;
the matching unit is used for matching the data in the Fields [ ] array according to the obtained message attribute types.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450645A (en) * | 2015-12-01 | 2016-03-30 | 上海汽车集团股份有限公司 | Data transmission method for vehicle-mounted automatic diagnosis system |
| CN107733875A (en) * | 2017-09-21 | 2018-02-23 | 深圳市盛路物联通讯技术有限公司 | A kind of data transmission method and system |
| CN108833562A (en) * | 2018-06-26 | 2018-11-16 | 南通森雅商务服务有限公司 | A kind of transmission and management system based on http traffic |
| CN109088874A (en) * | 2018-08-21 | 2018-12-25 | 北京睦合达信息技术股份有限公司 | A kind of data transmission method and equipment |
| CN111028384A (en) * | 2019-12-12 | 2020-04-17 | 苏州智加科技有限公司 | Intelligent fault classification method and system for automatic driving vehicle |
| CN111223283A (en) * | 2019-12-31 | 2020-06-02 | 南京陶特思软件科技有限公司 | Multi-path data classification type big data acquisition method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190095762A1 (en) * | 2017-09-25 | 2019-03-28 | Gyrfalcon Technology Inc. | Communications Between Internet of Things Devices Using A Two-dimensional Symbol Containing Multiple Ideograms |
-
2020
- 2020-07-24 CN CN202010725482.2A patent/CN111935250B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105450645A (en) * | 2015-12-01 | 2016-03-30 | 上海汽车集团股份有限公司 | Data transmission method for vehicle-mounted automatic diagnosis system |
| CN107733875A (en) * | 2017-09-21 | 2018-02-23 | 深圳市盛路物联通讯技术有限公司 | A kind of data transmission method and system |
| CN108833562A (en) * | 2018-06-26 | 2018-11-16 | 南通森雅商务服务有限公司 | A kind of transmission and management system based on http traffic |
| CN109088874A (en) * | 2018-08-21 | 2018-12-25 | 北京睦合达信息技术股份有限公司 | A kind of data transmission method and equipment |
| CN111028384A (en) * | 2019-12-12 | 2020-04-17 | 苏州智加科技有限公司 | Intelligent fault classification method and system for automatic driving vehicle |
| CN111223283A (en) * | 2019-12-31 | 2020-06-02 | 南京陶特思软件科技有限公司 | Multi-path data classification type big data acquisition method |
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