As interest in autonomous driving and advanced driver-assistance systems (ADAS) has grown, various sensing technologies have been developed to accurately determine the position and situation of surrounding vehicles and objects. In particular, light detection and ranging (LiDAR) sensors have attracted attention and are widely used in autonomous driving and ADAS because of their accuracy and reliability. However, when LiDAR sensors are used on a single vehicle, they can encounter blind spots caused by obstacles, which limits the detection of the environment. To overcome this issue, a method that can register and identify objects using LiDAR data from multiple vehicles in real-time is needed. Conventional artificial intelligence and iterative closest point (ICP) approaches need faster processing speed for practical use. Therefore, this work proposes an object-based single-point ICP (SP-ICP) which enables faster processing while maintaining accuracy using only a single point centered on each of the objects.

This paper presents the development results for the Wireshark dissector of the Ethereum Node Discovery Protocol for monitoring and analyzing Ethereum P2P networks. The new version v5 of the Ethereum Node Discovery Protocol applied encryption to the traffic. Therefore, the Wireshark Node Discovery Protocol based on previous versions, such as the v4 dissector, can no longer be used to analyze the network. This paper develops a dissector that interprets encrypted packets of the Node Discovery Protocol based on Ethereum Node Discovery Protocol v5. Node Discovery Protocol analysis can be used to research network properties and improve network performance.