## 2 Background and Related Works
### Related Works  
[PERSON] (2018) uses the Hadoop ecosystem to propose a whole process for recognizing, collecting, storing, and analyzing data collected from vehicles as big data [15]. The proposed model consists of collecting all vehicle information through Flume, transmitting it to the HDFS through Kafka, and analyzing and processing it using Spark. This paper only proposes a model in which the Hadoop ecosystem can be applied to the connected car environment, and has the disadvantage of complexity of further use as all messages collected from the vehicles are stored in the HDFS without classification.  
Figure 3: Kafka system architecture.  
[PERSON] et al. (2017) intended to develop a driving environment prediction platform based on road traffic information collected for driver safety. The method is changed to collecting data in real-time from mobile-type sensors rather than from existing fixed-type sensors, thus allowing for various-big-data collection. The data are analyzed on a driving environment analysis platform based on the Hadoop ecosystem, and information is provided through a web environment. However, in the research, vehicle sensor data are limited to road surface temperature, humidity, and vehicle speed.  
Among the research on the structure of a platform that collects connected car data, [PERSON] (2018) collects vehicle interior data and passenger's body reaction information through OBD and smart watch. The collection and processing phases were designed using GS1, a global standard for business communication, to establish standards for sharing information collected and stored in a connected environment with the outside world. However, the platform proposed in the paper focuses on collecting vehicle information and has the disadvantage that messages shared among connected units are limited to simple vehicle status information such as driving and stopping times.  
[PERSON] et al. (2020) aimed to implement a platform for developing connected car services, where the platform users could access data collected from connected cars [16]. Applications developed on the platform directly deliver messages such as BSM to a connected car. Connected car data collected by the platform are message-based information such as vehicle speed, location, and time.  
However, in the existing research, autonomous driving environment data are defined as CAN data, speed, and location information collected from the in-vehicle network. A majority of the data collected from a vehicle only serves to deliver the vehicle driving information rather than to derive new information based on the data. Therefore, there is a need for an additional method to utilize vehicle data, and for this, it is necessary to collect various vehicle data such as images and LiDAR data.  
[PERSON] et al. (2019) built a C-ITS application for safety using LDM [17]. The C-ITS application proposed in the paper seeks to collect vehicle driving information without equipment by utilizing infrastructure such as ORU installed on the road.  
When developing LDM, [PERSON] (2020) points out the limitations of the evaluation method focusing on rapid information delivery rather than information reliability [18]. He proposes a new evaluation method to overcome these limitations.  
In the case of existing studies on LDM, the focus is on developing and evaluating the LDM performance. However, research on the process and application of generating information to be provided by LDM is insufficient. Therefore, it is necessary to propose a platform that can apply an algorithm based on various connected car big data and their usage.  
[PERSON] et al. (2019) used black box images to detect traffic accidents [19]. It has been shown that traffic accidents or abnormal situations can be grasped from a dynamic camera, rather than a fixed camera image such as CCTV. Therefore, road accidents can be identified with vision data of autonomous vehicles, and vision data should be collected and used on a platform.