## 1 Introduction  
As autonomous driving technology has rapidly developed and become closer to commercialization, ensuring that stability and reliability of vehicle control and communication technologies are getting more important [1; 2]. In particular, the quantity of data exchanged in a vehicle has increased exponentially owing to sensors, such as cameras, LiDAR, and radar, for recognizing other vehicles and road conditions and controlling the vehicle, in addition to external communication via V2X technology [3; 4]. This increase in the amount of data inside of a vehicle has led to limitations in existing in-vehicle network (IVN) protocols such as controller area network (CAN), local interconnect network (LIN), and FlexRay. Thus, a new communication protocol that ensures high bandwidth is required as a solution. Ethernet is one such communication technology that provides a high bandwidth [5]. However, Ethernet tends to cost more because it requires connection equipment, such as switches, and uses a 1:1 connection. It also introduces problems such as providing a higher performance than necessary for certain devices. Furthermore, many sensors simply do not have an option to connect to an Ethernet. Accordingly, a more realistic alternative, at present, is to use both a conventional vehicle network such as CAN and a high-bandwidth network such as Ethernet, for which a gateway connects the different networks [6]. The performance of an autonomous vehicle, which integrates and processes the data measured from various sensors, may vary depending on whether there is time information of the sensor measurement [7; 8; 9; 10]. This is because the controller may have temporal distortion, that is, it is trying to perceive its situation by patching up numerous pieces of information that may be captured at different instants. In contrast, sensor measurements with time information allow the autonomous driving controller to be aware of time differences among the measurements and to compensate for the time differences for better perception of the situation. Therefore, to improve autonomous vehicle performance, increasing research is being conducted concerning time synchronization among the sensors and controllers connected via communication networks. For widely-used CAN networks, ECUs (electronic controller units) can be synchronized by using AutoSAR 4.2.2, which is a well-known middleware for ECUs [11; 12]. Time synchronization with AutoSAR has a master and slave structure; the slave calculates the delay and offset and corrects its own clock via two messages transmitted from the master. Within this process, time can be synchronized, such that the clock error between the master and slave is within a maximum of 10 \(\upmu\)s [11; 12]. However, AutoSAR requires quite significant computing resources, which makes it difficult to be implemented on every CAN node. In a FlexRay network, the time synchronization is based on the fact that the send and receive time points of all static messages are known to each FlexRay node. All FlexRay nodes compare the a priori known time points with the time points of synchronization messages actually received. Then FlexRay nodes create a sorted list of differences, from which they compute their offset correction value using the fault tolerant midpoint (FTM) algorithm [13]. Time-triggered Ethernet (TT-Ethernet) adopts a distributed time-synchronization algorithm and maintains global time by exchanging protocol control frames (PCFs) among synchronization components [14]. Synchronization components include a synchronization master (SM), a synchronization client (SC), and a compression master (CM). The synchronization process is as follows: first, the SM sends PCFs to the CM, and the CM calculates the average of PCF reception times and sends them to SMs and SCs by loading them into the new PCF. The new PCF is then used to calibrate nodes clocks [15].