## 1 Introduction  
Due to the introduction of the Fourth Industrial Revolution, information and communication technology (ICT) technology has been applied in various industries and is being developed. Among them, the automobile industry is actively utilizing internet of things (IoT) technology for autonomous vehicles. In addition, many automotive companies around the world are busy developing their technology through various competitions and interactions. Autonomous driving technology is divided into 0-5 levels. Level 0 means no autonomous driving. Level 1 is a state where one autonomous driving technology is applied. In level 2, an advanced driver assistance system (ADAS) on the vehicle can itself actually control both steering and braking/accelerating simultaneously under some circumstances. The human driver must continue to pay full attention at all times and perform the rest of the driving tasks. Level 3 is incomplete but is capable of autonomous driving and requires the driver to be ready to drive in an emergency. Level 4 is the stage in which the automatic driving system (ADS) of a vehicle performs all the driving tasks and monitors the driving environment in specific situations. The driver does not need to pay attention in such situations. Level 5 is a fully autonomous car that does not require a person to drive at all [1]. In the automobile industry, development has been achieved satisfying level 4, but commercialization has not yet been achieved. In order to commercialize autonomous unmanned vehicles, there are several problems to be solved such as the stabilization of software and the perception change about safety. The first problem to be solved is to improve a communication environment. The autonomous vehicle is equipped with a variety of internal sensors to collect data, and the collected data is exchanged with the Cloud server using a vehicle communication protocol. At this time, the Cloud server combines the data received from the sensor with various algorithm formulas to organically transmit commands to the vehicle or generate new data. There are various sensors in the vehicle to collect data. As the number of sensors gets increased, a vehicle uses several kinds of protocols for communication. Existing vehicle gateways, however, did not support some of the major protocols used in the vehicle, or rapid communication. The second problem is to extend the scope of vehicle diagnosis. Autonomous vehicles perform their own diagnosis and driving simultaneously. Since existing vehicles use on board diagnostics (OBD) to perform fault-diagnosis, there is a problem in processing speed and it is impossible to diagnose a large amount of sensor data. Recent studies use neural network models to diagnose a vehicle, but they diagnose only specific parts, not the overall condition of the vehicle. In addition, special hardware equipment is required to run neural network models in the vehicle in real time. This paper presents the following main functions to solve these problems. First, the multi-protocol integrated gateway module (MIGM) in the lightweight autonomous vehicle self-diagnosis (LAVS) acts as an internal gateway to existing vehicles, ensures the traffic of vehicle communication in real time, and manages sensor messages for vehicle diagnosis. Second, to improve autonomous vehicle processing speed and accuracy, the in-vehicle diagnosis module (In-VDM) in the LAVS combines a random forest with a neural network model. It not only diagnoses the parts of a vehicle by learning a random forest model, but also the total condition of a vehicle by learning a neural network model. Therefore, the LAVS can reduce the overload of vehicle-to-cloud(V2C) communication because it delivers only diagnostic results to the Cloud and improve the safety of an autonomous vehicle. The composition of this paper is as follows. Section 2 describes the existing studies related to the LAVS in this paper. Section 3 details the structure and operation of the LAVS. Section 4 compares it with the existing methods to analyze the performance.