KR20250029103A - Vehicle data collection and transmission apparatus and method - Google Patents

Abstract

One aspect of the present invention discloses a device for collecting vehicle data. The device includes a plurality of sensors installed in a vehicle, a network module configured to communicate with at least one of internal and external equipment, an environment analyzer for analyzing an environment of a vehicle related to autonomous driving using information acquired from at least some of the plurality of sensors and the network module, a sensor controller for controlling operations of at least some of the plurality of sensors based on environmental analysis results from the environment analyzer, and a collection module for storing data generated by at least some of the plurality of sensors controlled by the sensor controller.

Description

{VEHICLE DATA COLLECTION AND TRANSMISSION APPARATUS AND METHOD}

The present invention relates to a device for collecting and transmitting vehicle data, and more specifically, to a device for efficiently collecting and transmitting vehicle data for monitoring abnormal condition analysis of a vehicle driving at a predetermined speed.

Recently, much research has been conducted on technologies to efficiently collect vehicle-related sensing data through vehicle communication and transmit it to external servers.

Figure 1 is a conceptual diagram showing a system for transmitting conventional vehicle data to an external server.

Referring to FIG. 1, a plurality of vehicles (110-1, 110-2, 110-3) transmit data to an external server (130-1, 130-2) via a network (120). At this time, the vehicles (110-1, 110-2, 110-3) can perform data processing and communication via various vehicle communications (V2X), such as vehicle-to-base station communication (V2I) or vehicle-to-vehicle communication (V2V). The network (120) may be a network in which various communication protocols, such as 5G, 4G, and Wi-Fi, are mixed. The servers ( ^130-1 , 130-2) are mostly ^3rd party collection devices, but in some embodiments, OEM (Original Equipment Manufacturer) collection devices and ^3rd party collection devices are distinguished and a communication method between them is proposed.

Figure 2 is a conceptual diagram illustrating a detailed system for transmitting vehicle data.

Referring to FIG. 2, the system is composed of a sensor unit that detects various data related to a vehicle, a processing unit that processes the sensing data, a network device for transmitting vehicle data to the outside or receiving external data from the vehicle, and a network and external data collection device.

Referring to previous studies related to these systems, the key to collecting data generated from automobiles is to overcome the characteristics of automobile data (heterogeneous) and network characteristics (limited network resources), and therefore, various studies related to this are being conducted.

In particular, applications that utilize data generated from autonomous vehicles are largely divided into real-time applications and non-real-time applications. Real-time applications of data are required when services related to vehicle safety are needed, such as when autonomous vehicles are in an emergency or when there are dangerous elements for autonomous driving on a specific road section, and non-real-time applications of data are required when services related to vehicle convenience are needed, such as fuel efficiency analysis of the vehicle, suggestions for alternative routes, or map updates.

Conventional vehicle communication methods have a common structure of defining a data structure according to the type of each data and transmitting it through a network. However, if this method is applied to the safety of autonomous vehicles, unnecessary data may be transmitted without considering the environment in which the vehicle is located. For example, when a vehicle is traveling straight at high speed on a highway, radar and camera information in front of the vehicle are the most important, and rear camera/all-round lidar may not be so important. If data is transmitted by equally considering the importance of all sensing information without considering this, the efficiency of data collection and transmission will decrease.

That is, the data collected from autonomous vehicles is fixedly structured according to its format, and since this data is stored and transmitted in the same format when applied in real time or not in real time, there is a problem that large amounts of data collected through lidar or cameras are transmitted over the network even though they are unnecessary depending on the situation, resulting in a waste of network resources. On the other hand, since the data format is fixed, there is also a problem that the analysis cannot be performed efficiently because data that requires more precise analysis is compressed at a low resolution or low bit rate and transmitted.

An object of one aspect of the present invention for solving the above-described problem is to provide a vehicle data collection device that determines an abnormal state and emergency situation of an autonomous vehicle (including a real-time abnormal state and a non-real-time abnormal state), and stores and transmits data by adjusting the bit rate and resolution of a vehicle-related sensor according to the determined state.

In addition, an object according to another aspect of the present invention is to provide a device that stores various sensor data of an autonomous vehicle based on real-time/non-real-time adaptivity according to the environment of the vehicle and stores the data in an environment-adaptive manner, and at this time, stores and transmits the data in a form that is optimal for judging and analyzing abnormal conditions and emergency situations of an autonomous vehicle by changing the data format.

According to one aspect of the present invention for achieving the above purpose, a device for collecting vehicle data may include a plurality of sensors installed in a vehicle, a network module configured to communicate with at least one of internal and external equipment, an environment analyzer that analyzes an environment of a vehicle related to autonomous driving using information acquired from at least some of the plurality of sensors and the network module, a sensor controller that controls operations of at least some of the plurality of sensors based on the results of the environmental analysis by the environment analyzer, and a collection module that stores data generated by at least some of the plurality of sensors controlled by the sensor controller.

The above sensor controller selects a sensor among the plurality of sensors that requires adjustment of sensing operation based on the environmental analysis results, and can adjust at least one of a bit rate, a sampling time, a data amount, a data generation cycle, quantization, encoding, and resolution of the selected sensor.

The above sensor controller may select a sensor among the plurality of sensors, based on the environmental analysis results, that requires adjustment of a sensing operation, and may control at least one of a bit rate, a sampling time, a data amount, a data generation cycle, quantization, encoding, and a resolution of the selected sensor to command the collection module to receive data from the selected sensor.

The above plurality of sensors may include a safety sensor unit including at least one of a camera, a radar, and a lidar, and a vehicle sensor unit that senses at least one of vehicle speed, tire air pressure, a steering angle, whether an airbag is opened or closed, and whether brake force and ESC (Electric Stability Control) are in operation.

The above environmental analyzer analyzes the environment by each category using categorized information, and the categories for environmental analysis may be categorized into at least two or more of regional category information generated based on the current driving location of the vehicle using map data, speed category information related to the current driving speed of the vehicle, network category information about the communication environment related to the vehicle, and traffic volume category information about the traffic situation related to the vehicle.

The categories for the above environmental analysis may further include event category information for special situations related to traffic.

The above network category information may include network traffic category information and network delay category information associated with network delay.

Information for each category is divided into at least two attributes, and the environmental analyzer can define the current environmental type of the vehicle by interpreting the divided attributes for each of the plurality of categories.

The above environmental analyzer can define the current environmental type of the vehicle by matching a combination of distinct attributes for each of the plurality of categories to a predefined environmental type using a pre-stored table.

In response to the current environment type of the vehicle defined above, an optimal sensor adjustment method can be determined.

In response to the above area category information being classified as a downtown attribute and the above traffic volume category information being classified as an increase or large attribute, the sensor controller can be controlled to perform at least one of (i) a coordination operation that strengthens the operation of the vehicle surrounding sensors more than the remote sensors and (ii) a coordination operation that increases the number of detected objects around the vehicle.

In response to the above-mentioned area category information being classified as a downtown attribute, the above-mentioned traffic category information being classified as a decrease or small attribute, and the above-mentioned network category information being classified as an increase or large attribute, the sensor controller can be controlled to perform a coordination operation that sets a high priority for a sensor requiring real-time transmission.

The above sensor controller may include a sensor parameter update unit that updates sensor parameters based on current environment type information of the vehicle defined by the environment analyzer.

The above sensor parameter update unit can update the sensor parameters using at least one of (i) an optimization model of a cost function, (ii) a machine learning model, and (iii) a preset rule, according to the current environment type information.

The above collection module may include a sensor receiving unit that receives sensing data generated by at least some of the plurality of sensors that perform operations controlled by the sensor controller, and a sensor DB management unit that temporarily stores data received from the sensor receiving unit in the storage.

The above sensor DB management unit can (i) store the received data in the storage in an independent storage space for each individual sensor, or (ii) sort the received data in the form of a single package and store it in the storage.

The above network module may include a network traffic investigation unit that investigates network traffic between the vehicle and the server, a network delay investigation unit that investigates network delay between the vehicle and the server, and a network throughput investigation unit that investigates network throughput between the vehicle and the server.

The above network module may further include a network status sharer that obtains current network status information and past network status information from the network traffic investigation unit, the network delay investigation unit, and the network throughput investigation unit, creates network status information including a network status change trend, and transmits the network status information to the environment analyzer and the collection module.

According to another aspect of the present invention for achieving the above object, a method for collecting vehicle data may include a step of obtaining at least one of sensing data and network-related data from at least some of a plurality of sensors and network modules installed in a vehicle (the network module is configured to communicate with at least one of internal and external equipment), a step of analyzing an environment of the vehicle related to autonomous driving using the obtained information, a step of controlling an operation of at least some of the plurality of sensors based on a result of the environmental analysis, and a step of storing data generated by at least some of the plurality of sensors, the operation of which is controlled.

According to the vehicle data collection device of the present invention, no matter what high-performance/high-resolution sensor is installed in an autonomous vehicle, it has the effect of enabling the collection and transmission of environmentally adaptively sensed data without difficulty.

Figure 1 is a conceptual diagram showing a system for transmitting conventional vehicle data to an external server.
Figure 2 is a conceptual diagram illustrating a detailed system for transmitting vehicle data.
FIG. 3 is a block diagram schematically illustrating a vehicle data collection device according to one embodiment of the present invention.
Figure 4 is a detailed block diagram showing the environmental analyzer of Figure 3 in more detail.
Figure 5 is a table showing the criteria for environmental categories and their associated attributes.
Figure 6 is a table showing the matching relationship of the method of adjusting the sensors in the vehicle in response to the defined environment.
Fig. 7 is a detailed block diagram showing the sensor controller of Fig. 3 in more detail.
Fig. 8 is a conceptual diagram for explaining the process of controlling sampling and quantization operations of sensor data using sensor parameter control information of the sensor controller of Fig. 7.
Figure 9 is a detailed block diagram showing the collection module of Figure 3 in more detail.
Figure 10 is a detailed block diagram showing the network module of Figure 3 in more detail.

The present invention may have various modifications and embodiments, and specific embodiments are illustrated in the drawings and described in detail.

However, this is not intended to limit the present invention to specific embodiments, but should be understood to include all modifications, equivalents, or substitutes included in the spirit and technical scope of the present invention.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are only used to distinguish one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component. The term and/or includes any combination of a plurality of related described items or any item among a plurality of related described items.

When it is said that a component is "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components in between. On the other hand, when it is said that a component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprises" or "has" and the like are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms defined in commonly used dictionaries, such as those defined in common dictionaries, should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and will not be interpreted in an idealized or overly formal sense unless expressly defined in this application.

Hereinafter, with reference to the attached drawings, a preferred embodiment of the present invention will be described in more detail. In order to facilitate an overall understanding in describing the present invention, the same reference numerals are used for the same components in the drawings, and redundant descriptions of the same components are omitted.

FIG. 3 is a block diagram schematically showing a vehicle data collection device according to one embodiment of the present invention. As illustrated in FIG. 3, the vehicle data collection device according to one embodiment of the present invention largely includes a sensor unit, a processing unit, and a network unit. The sensor unit includes a safety sensor (310: Safety Sensor) and a vehicle sensor (312: Vehicle Sensor), the processing unit includes an environmental analyzer (320), a sensor controller (322), a collection module (326), and a storage (328), and the network unit may include a network module (330).

Referring to FIG. 3, the sensor unit may be composed of a plurality of sensors that detect various information related to the vehicle's surroundings, such as the front, rear, and side of the vehicle, and driving or safety of the vehicle. More specifically, the sensor unit may be composed of a safety sensor (310) and a vehicle sensor (312).

Safety sensors (310) installed in autonomous vehicles include multiple cameras, multiple LiDARs, multiple radars, etc. Most of these are composed of multiple sensors that produce large amounts of data. The sensor unit outputs raw data and also outputs various recognition results processed from raw data.

The vehicle sensor (312) includes sensors of various ECUs (Electronic Control Units) installed in the vehicle. The information generated by the vehicle sensor (312) includes information on various vehicle operation states such as vehicle speed, tire pressure, steering angle, whether the air bag is opened or closed, brake force, and whether ESC (Electric Stability Control) is operating.

The processing unit may include, as described above, an environmental analyzer (320), a sensor controller (322), a collection module (326), and a storage (328). The processing unit may be implemented with one or more processors, and these may be implemented as individual processors according to their functions, implemented as a combination of multiple processors, or implemented in a form in which a plurality of functional blocks are integrated into one processor.

First, the environment analyzer (320) analyzes the current autonomous driving environment of the vehicle by referring to necessary information inside/outside the system, and transmits the analysis result to the sensor controller (322). The autonomous driving environment includes the current driving environment of the vehicle, and may be information analyzed by comprehensively encompassing all information that may affect the driving and communication environment of the vehicle, such as sensing information from the vehicle's safety sensor (310) and vehicle sensor (312), vehicle location information, map data information, traffic information, network information, weather information, and/or other event information. For such environmental analysis of the autonomous vehicle, the current location information of the vehicle, route information to the destination, surrounding traffic information, etc. may be required, and therefore the environment analyzer (320) may utilize map data (324). The analysis result may be output as environment type information predefined for a specific type of autonomous vehicle environment.

The sensor controller (322) analyzes the autonomous driving environment type information (and the corresponding sensor adjustment method information) analyzed by the environment analyzer (320), sets the importance of each sensor according to a set rule, and performs a function of adjusting the resolution, sampling time, etc. of each sensor. At this time, the sensor controller (322) may directly transmit a signal to each sensor to adjust the sensor parameters, or may transmit a signal to the collection module (326) to adjust the resolution/sampling time, etc. of the signal input from the sensor. That is, the function of adjusting the amount of data or resolution of each sensor depending on the sensor type may be performed by an individual sensor or by the collection module (326).

The collection module (326) may sort sensor signals transmitted from various sensors and store them in internal storage (328) (which may be referred to as an “internal storage device”), or may transmit them as they are to an external server (not shown) via a network module (330). At this time, the internal storage (328) is a storage space that temporarily stores collected information for transmission, and may be an EDR (Event Data Recorder) and/or DSSAD (Data Storage System for Autonomous Driving) installed in an autonomous vehicle.

The network module (330) is responsible for the communication function with the server. The network module (330) can perform the function of monitoring the network traffic status and the network delay status of the route currently connected to the server. Since information related to the network can also be considered as environmental information of the autonomous vehicle, the network module (330) can perform the function of transmitting such information to the environmental analyzer (320).

Fig. 4 is a detailed block diagram showing the environmental analyzer of Fig. 3 in more detail. As shown in Fig. 4, the environmental analyzer (400) may include map data (402), an environmental information receiving unit (410), an environmental analysis unit (420), and an environmental definition unit (430).

Referring to FIG. 4, the environmental analyzer (400) may include its own database (DB), storage, or memory, which may include map data (402) such as a digital map. The digital map may include information on the physical shape of the road as well as the properties of the road such as urban, suburban, and rural areas. Such map data (402) may be updated from the outside. The map data (402) may be a full map, or may be implemented as a partial map according to the current vehicle location. This may receive information from a map system used by an autonomous vehicle.

The environmental information receiving unit (410) can receive information about the current location of the autonomous vehicle (including latitude and longitude coordinates or coordinates mapped on a map, etc.) from the autonomous driving controller. In addition, the environmental information receiving unit (410) can receive environmental information of the area in which the vehicle is driving. The environmental information of the area includes not only server-related network information transmitted from the server, such as network traffic changes with the server and delay changes with the server, but also network-related information, such as surrounding base station locations and throughput information transmitted from a network provider. In addition, information related to vehicles and traffic, such as traffic traffic, accidents, construction information, and traffic information, can also be received.

The environmental analysis unit (420) analyzes the environment using the environmental information receiving unit (410) and information stored in its own data storage space. The environmental analysis unit (420) can divide various environments into categories as follows. The environmental categories distinguished by the environmental analysis unit (420) are described in more detail with reference to FIG. 5 below.

Figure 5 is a table showing the criteria for environmental categories and their associated attributes.

Referring to FIG. 5, the environment category may be composed of region, speed, network information, traffic volume, and event categories. At this time, the network information category may be composed of network traffic information and network delay information. In one example, not all of the above categories must be considered, and it is preferable that one or more, more preferably two or more, categories be considered.

Information in the local category can be divided into three attributes: urban, suburban, and rural, based on the current location where the vehicle is driving.

Information in the speed category can be divided into attributes indicating high-speed driving, attributes indicating normal driving, and attributes indicating low-speed driving. For example, if driving at a speed of 50 km/h or less, it can be divided into a low-speed driving attribute, if driving at a speed of 50 km/h to 100 km/h, it can be divided into a normal driving attribute, and if driving at a speed of 100 km/h or more, it can be divided into a high-speed driving attribute. At this time, the threshold values do not necessarily have to be set to 50 km/h and 100 km/h, and may be set to other values.

Next, the information of the network traffic category can be expressed as the current network traffic number information. In addition, the network traffic number of the preset previous time section and the network traffic number of the current section can be compared, and if the comparison result is below the threshold value, it can be determined as a normal attribute, if the comparison result is increasing by more than the threshold value, it can be determined as an "increase" attribute, and if the comparison result is decreasing by more than the threshold value, it can be determined as a "decrease" attribute. In another example, the current network traffic number can be directly compared with the threshold value, and can be classified into the attributes of "normal", "a lot", and "a little (or none)".

Information in the network delay category can be analyzed as current network delay number information. In addition, the network delay number of a preset previous time can be compared with the current network delay number, and if the comparison result is below a threshold value, it can be determined as a normal attribute. If the comparison result is increasing by more than the threshold value, it can be determined as an "increase" attribute. If the comparison result is decreasing by more than the threshold value, it can be determined as a "decrease" attribute. In another example, the current network delay number can be directly compared with the threshold value, and can be classified into the attributes of "normal", "a lot", and "a little (or none)".

In addition, information in the traffic category can be expressed as three attributes by judging whether the traffic volume is normal, increasing, or decreasing based on the number of vehicles driving within a preset reference range in a specific area (e.g., the area of the current vehicle location, the area on the route, or the area of the destination, etc.). Alternatively, the environment analysis unit (420) can control whether the traffic volume is low, normal, or high based only on the current traffic volume without performing a comparison with the traffic volume of the previous time section, by comparing it with a preset threshold value, and expressing this as three attributes as well.

Lastly, information in the event category can be categorized into various attributes as information that provides information on situations that suddenly occur in a specific area, such as accidents, construction, slips, equipment failures, and ambulances.

However, the categories of information for environmental analysis do not necessarily have to consist of only the six categories above. They can consist of fewer than six categories, or additional categories (e.g., weather, seasons, etc.) can be added to make a total of seven or more categories.

In addition, although in this embodiment, it is described that one category is divided into three properties, it will be obvious to those skilled in the art that the number of properties may be divided into two properties, four properties or more properties by threshold(s) set differently according to user input.

The environment definition unit (430) can define a representative environment type using the environment information analyzed by category in the environment analysis unit (420). To define the representative environment type, all of the environment information analyzed by category in the environment analysis unit (420) may be utilized, or only some of the characteristic environment information may be used. The environment type may be defined using a pre-set table. In one example, a method may be used to define the environment type of the vehicle more precisely by executing a deep learning model using data defining past environment types as learning data. The environment type may be defined by various interpretations based on at least some of the various autonomous driving-related vehicle environment information. Various sensor adjustment methods may be determined according to the defined environment type. The determination of the environment type and the matching relationship between the environment type and the sensor adjustment method are described in more detail with reference to FIG. 6.

Figure 6 is a table showing the matching relationship of the method of adjusting the sensors in the vehicle in response to the defined environment.

Referring to Fig. 6, the environment definition unit obtains environmental information by category from the environment analysis unit and matches it to a specific type of environment type. According to the environment definition, the following representativeness can be exemplified for status analysis or safety analysis of the current autonomous vehicle.

For example, if you are currently driving on a highway at high speed, the traffic volume is moderate, but there is highway construction ahead, the environment definition unit can define the environment as one that requires a lot of forward visibility. That is, when the index of the environment type is defined as "Environment Type X," this can be defined as "data sensing the front of the vehicle is important, and the network condition is also good."

Meanwhile, in the environment type of "low-speed driving in urban areas", it is desirable to consider collisions with pedestrians/cycles as the most important factor. To this end, high-resolution information from sensors capable of detecting objects within several meters (m) around the vehicle, such as close-range radar, close-range lidar, ultrasonic sensors, and cameras for Around View Monitoring, is important. In addition, at such times, data required for high-speed driving, such as long-range lidar, front ADAS camera, and tire pressure information, are not necessary at all, or can be transmitted in low resolution. Hereinafter, the environment types will be defined and the sensor adjustment directions corresponding thereto will be described in more detail with reference to the embodiments of FIG. 6.

In the embodiment of Fig. 6, environment type 1 is classified as an “urban” attribute in the area category, a “normal” attribute in the speed category, a “normal” attribute in the network traffic category, an “increase” attribute in the network delay category, an “increase” attribute in the traffic volume category, and finally, an “none” attribute in the event category, and additionally, a “clear” attribute in the weather category. The environment definition unit defines this environment as an “environment type in which traffic volume increases while driving at a normal speed in an urban area,” and in response to this, the sensor adjustment method can be matched by reinforcing the surrounding sensors rather than the remote sensor information, and maximizing the number of surrounding objects detected by the ADAS sensors. The definition of the environment type and the sensor adjustment method corresponding to it can be stored in advance in the form of a (look-up) table. The environment definition unit defines the environment type based on the results of the environmental analysis by category from the environment analysis unit, and retrieves the sensor adjustment method corresponding to the defined environment type using the table. Thereafter, the sensor adjustment unit transmits a corresponding signal to each sensor based on the retrieved sensor adjustment method.

Next, environment type 2 is distinguished by the "urban" attribute in the area category, the "slow" attribute in the speed category, the "increase" attribute in the network traffic category, the "decrease" attribute in the network delay category, the "decrease" attribute in the traffic volume category, and finally, the "none" attribute in the event category, and additionally, the "cloud" attribute in the weather category. The environment definition unit defines this environment as an "environment type where there is no traffic but network traffic increases during low-speed driving in an urban area", and in response to this, the sensor adjustment method can be matched by setting the priority of sensors that should be transmitted in real time rather than ADAS sensors because they are currently low risk to the vehicle to be transmitted preferentially.

Environment type 3 is classified by the "urban" attribute in the area category, the "slow" attribute in the speed category, the "increase" attribute in the network traffic category, the "increase" attribute in the network delay category, the "increase" attribute in the traffic volume category, and finally, the "accident" attribute in the event category, and additionally, the "sunny" attribute in the weather category. The environment definition unit defines this environment as "an environment type in which all cars are driving at low speeds because there is currently an accident somewhere ahead", and in response, the sensor adjustment method can be matched by collecting data by increasing the resolution and clarity of all cameras in the vehicle to determine the situation, such as what kind of accident is ahead, what the current situation is, and whether an ambulance or a fire truck is needed, although the risk of an accident is low.

Environment type 4 is classified as "sub-urban" in the area category, "normal" in the speed category, "normal" in the network traffic category, "none" in the network delay category, "normal" in the traffic volume category, and finally, "slip" in the event category, and additionally, "rain" in the weather category. The environment definition unit defines this environment as an "environment type where a slip event is occurring on a rainy local road", and can match the sensor adjustment method accordingly: since it is a rainy area, the lidar and camera have lower resolutions, and the vehicle speed and the wheel speed of all four wheels have higher resolutions and are synchronized and stored.

Environment type 5 is classified by the "urban" attribute in the area category, the "slow" attribute in the speed category, the "normal" attribute in the network traffic category, the "none" attribute in the network delay category, the "none" attribute in the traffic volume category, and finally, the "right turn signal" attribute in the event category, and additionally, the "sunny" attribute in the weather category. The environment definition unit can define this environment as an "environment type in which a right turn is being attempted at a low speed in an urban area", and in response, the sensor adjustment method can be matched to turn off the rear sensor and collect data by maximizing the resolution of the front camera, right LiDAR, right Radar, and right AVM camera to prepare for accidents involving right-side pedestrians, bicycles, and motorcycles.

Environment type 6 is classified as "urban" in the area category, "normal" in the speed category, "none" in the network traffic category, "increase" in the network delay category, "none" in the traffic volume category, and finally, "none" in the event category, and additionally, "sunny" in the weather category. The environment definition unit defines this environment as an "environment type where there is no abnormality, but delay is gradually increasing", and in response, the sensor adjustment method can be matched to store more detailed network information for the section in preparation for possible issues with bandwidth or throughput, judging from the fact that there is no abnormality, but network delay is gradually increasing.

As described above, the vehicle data collection device according to one embodiment of the present invention can match a specific sensor adjustment method in response to an environment type defined by a specific index based on a pre-stored table, thereby deriving an optimal solution that synthesizes various categories of environmental information. In addition, by appropriately signal-processing the derived optimal solution to each sensor through a sensor controller, the operations of sensors related to the vehicle can be quickly adjusted, and through this, sensing data can be efficiently stored and transmitted externally.

Fig. 7 is a detailed block diagram showing the sensor controller of Fig. 3 in more detail. As shown in Fig. 7, the sensor controller (700) may include a sensor parameter update unit (710), a safety sensor setting unit (730), and a vehicle sensor setting unit (740).

Referring to FIG. 7, the sensor controller (700) adjusts each sensor according to the type of environment analyzed and defined by the environmental analyzer.

First, the sensor parameter update unit (710) sets the operation parameters of each sensor using the environment type analyzed and defined by the environment analyzer. The operation parameters of each sensor are recorded in the sensor parameter document (720). The sensor parameter document (720) can be updated by the sensor parameter update unit (710) in real time, periodically, or aperiodically. For example, if the currently defined environment type and the corresponding solution indicate that the information of the forward long-range radar is not so important, the sensor parameter document (720) can be updated to instruct the sensor to lower the signal generation period of the forward long-range radar from 100 ms to 10 ms, or the sensor parameter document (720) can be updated to stop the signal altogether. That is, the signal generation period and sensor parameters of each sensor can be varied according to the sensor adjustment method determined in response to the environment type.

The goal of adjusting these sensor parameters is to optimally select various parameters (such as data generation volume and cycle) of the sensors selected according to the environmental definition in the current network bandwidth and delay. To this end, an optimization model that determines a cost function and optimizes it can be used. Alternatively, a method of deriving optimal parameters through machine learning from past data can be used. Furthermore, a method of deterministically deriving optimal parameters according to a predetermined rule (mathematical formula) can be used.

In this way, when the sensor parameter document (720) is updated, the safety sensor setting unit (730) periodically or upon receiving an update event sends a command to the sensor to actually manipulate the parameters of each safety sensor by referring to the sensor parameter document (720). At this time, the safety sensors include sensors used for autonomous driving, such as ADAS cameras, AVM cameras, various radars, and various lidars, and the connection method to these sensors may be in the form of a bus or a direct connection.

The vehicle sensor setting unit (740) also periodically or upon receiving an update event sends a command to the sensor to actually manipulate the parameters of each vehicle sensor by referring to the sensor parameter document (720). At this time, a CAN bus or the like can be used to set each vehicle sensor.

FIG. 8 is a conceptual diagram for explaining a process of controlling sampling and quantization operations of sensor data using sensor parameter control information of the sensor controller of FIG. 7.

Referring to FIG. 8, the sensor controller (810) can adjust the resolution and the amount of generated codes of each sensor according to the environmental definition. At this time, the adjustment of the resolution or amount of codes of the sensing data may be performed by directly controlling the operation of the sensor, but as in the embodiment of FIG. 8, sensor parameter adjustment information may be provided to the collection module (820) so that the collection module (820) can directly adjust the resolution, amount of generated codes, and/or period related to “sampling” or “quantization” or “encoding” of the data stream transmitted from the sensor. In other words, the amount and period of generated codes stored in a storage, or the amount and period of generated codes transmitted to an external server, may be adjusted through the collection module (820).

Fig. 9 is a detailed block diagram showing the collection module of Fig. 3 in more detail. As shown in Fig. 9, the collection module (900) may include a safety sensor receiving unit (910), a vehicle sensor receiving unit (920), a safety sensor DB management unit (930), a vehicle sensor DB management unit (940), and a transmission data management unit (960).

Referring to FIG. 9, the collection module (900) temporarily stores sensor data sent from various sensors or packetizes and transmits the data to the server.

First, the safety sensor receiver (910) receives sensing data from the safety sensor through preset parameters and manages connections with the safety sensors. In addition, it can receive sensing data by receiving information from the sensor controller and adjusting the bit rate, resolution, etc. of the sensor that is fixedly received. The vehicle sensor receiver (920) also performs the same operation as the safety sensor receiver (910) for the vehicle sensor.

The sensor DB management units (930, 940) temporarily store data from various sensors received in the DB (950) (which may be referred to as a storage or internal storage device). This can be controlled to store each individual sensor in a separate DB (950) space, or can be arranged in a package form and stored all at once.

The transmission data management unit (960) plays a role in transmitting at least some of the various sensor data being stored to the server according to server requests, network information, and/or environmental analysis results. That is, each sensor data stored in the DB (950) is selectively transmitted to the server.

FIG. 10 is a detailed block diagram showing the network module of FIG. 3 in more detail. As shown in FIG. 10, the network module (1000) may include a network traffic investigation unit (1010), a network delay investigation unit (1012), a network throughput investigation unit (1014), a network status sharing unit (1020), a data encoding unit (1030), a data packetization unit (1040), and a data serialization unit (1050).

Referring to FIG. 10, in order to investigate the current network status, the network traffic investigation unit (1010) can investigate the current network traffic between the vehicle and the server. Similarly, the network delay investigation unit (1012) can investigate the current network delay between the vehicle and the server, and the network throughput investigation unit (1014) investigates the current network throughput between the vehicle and the server and transmits it to the network status sharing unit (1020).

The network status sharing device (1020) creates network status information including changes in each network status based on the current network status and past status, and transmits this to the environment analyzer and collection module, respectively.

In addition, sensing data from each sensor based on the current environment analysis results transmitted from the collection module may be encoded, packetized, and data serialized and transmitted to the server, individually or in whole, through the data encoding unit (1030), the data packetization unit (1040), and the data serialization unit (1050).

Although the present invention has been described with reference to the drawings and embodiments, it does not mean that the scope of protection of the present invention is limited by the drawings or embodiments, and it will be understood that a person skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention as described in the following claims.

Claims (8)

In a device that processes vehicle data,
Multiple sensors installed on the vehicle;
A network module configured to communicate with at least one piece of equipment inside and outside the vehicle;
An analyzer that analyzes the state of a network related to driving of the vehicle using information obtained from the network module; and
Including a controller that controls the processing of data of at least some of the plurality of sensors based on the analysis results from the analyzer,
It further includes a data packetization unit that periodically transmits packets to an external server,
The above analyzer analyzes the current status of the network related to the driving of the vehicle by using information related to network delay.
The above network module receives information about the network delay status of the network path currently connected to the external server from the external server and transmits it to the analyzer.
The above analyzer analyzes the status of the currently connected network path based on information about the network delay status received from the external server,
The above controller is a vehicle data processing device that controls the processing of data of at least some of the plurality of sensors based on analysis information about the status of the currently connected network path. In paragraph 1,
A vehicle data processing device wherein the controller adjusts at least one of a bit-rate, a sampling time, a data amount, a data generation cycle, quantization, encoding, and a resolution of at least some of the plurality of sensors. In paragraph 1,
A vehicle data processing device, wherein the controller provides a signal to at least one of the plurality of sensors to increase or decrease at least one parameter of bit-rate, sampling time, data amount, data generation cycle, quantization, encoding, and resolution of at least some of the plurality of sensors. In paragraph 1,
The above regulator is a vehicle data processing device that selects data to be processed by considering the importance of some of the plurality of sensors based on the analysis results from the above analyzer. In the first paragraph, the plurality of sensors,
A safety sensor unit including at least one of a camera, a radar and a lidar; and
A vehicle data processing device including a vehicle sensor unit that senses at least one of vehicle speed, tire pressure, steering angle, whether an airbag is opened or closed, brake force, and whether ESC (Electric Stability Control) is operating. In paragraph 5,
The above vehicle sensor unit includes an ECU (Electronic Control Unit),
A vehicle data processing device, wherein the controller transmits a signal requesting adjustment of data processing of at least some of the plurality of sensors to the ECU. In a method of processing vehicle data,
A step of receiving information about the delay status of a network path currently connected to an external server from the external server;
A step of analyzing the current state of the network path using information on the delay state of the received network path; and
Comprising a step of controlling the processing of data of at least some of a plurality of sensors associated with the vehicle based on the analysis results of the current state of the network path,
Further comprising a step of periodically transmitting packets to the above external server,
The step of analyzing the current state of the above network path includes a step of analyzing the current state of the network related to the driving of the vehicle using information related to network delay,
The above receiving step includes a step of receiving information about the network delay status of the network path currently connected to the external server from the external server,
The above analyzing step includes a step of analyzing the status of the currently connected network path based on information about the network delay status received from the external server,
A vehicle data processing method, wherein the above-mentioned adjusting step includes a step of adjusting the processing of data of at least some of the plurality of sensors based on analysis information on the status of the currently connected network path. In a system that processes vehicle data,
A vehicle data processing device that receives information on the delay status of a network path currently connected to an external server from the external server, analyzes the current status of the network path using the information on the received delay status of the network path, and controls the processing of data of at least some of a plurality of sensors associated with the vehicle based on the analysis result of the current status of the network path; and
Including an external server that receives packets from the vehicle data processing device and provides information on the delay status of the network path to the vehicle data processing device,
The above vehicle data processing device periodically transmits packets to an external server,
The above vehicle data processing device analyzes the current status of the network related to the driving of the vehicle by using information related to network delay.
The above vehicle data processing device receives information about the network delay status of the network path currently connected to the external server from the external server,
The above vehicle data processing device analyzes the status of the currently connected network path based on information about the network delay status received from the external server,
A vehicle data processing system, wherein the vehicle data processing device controls the processing of data of at least some of the plurality of sensors based on analysis information about the status of the currently connected network path.

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