## 1 Introduction  
The self-driving, autonomous vehicle has been getting lots of attention, due to significant developmental efforts and dramatic progress made by companies such as Google. While general use of autonomous vehicles for widespread use on public roads is likely years away, these vehicles are already being employed in \"constrained\" applications, such as open-pit mines and farming. Among the many technologies that make autonomous vehicles possible is a combination of sensors and actuators, sophisticated algorithms, and powerful processors to execute software [1]. With Tesla'srecent announcement of a semi-autonomous vehicle and Google's strides in testing self-driving cars, we are beginning to see the first signs of a driverless future [2].  
A vehicle with autonomous driving capability may be configured to receive signal inputs from the sensors that monitor the vehicle's operations, surrounding objects, and road conditions in order to identify safety hazards and generate countermeasures to deal with various driving situations. The autonomous vehicle may also collect and record data from various information sources such as the cellular network, satellites, as well as user inputs such as a user's identification, destinations and routes of navigation requests, and vehicle operation preferences. A vehicle with autonomous driving capability may further be adapted to detect various potential hazardous conditions and issue warnings to the user. The potential hazardous condition may include, for example, the vehicle's approaching a sharp curve, nearby pedestrians, or icy roads, etc. Such vehicle may also be configured with mechanisms that take active steps to avoid these hazards; e.g., slowing down the vehicle or applying the brake, etc. [3].  
Cars have been diagnosing their own problems for decades, and to an extent, fixing themselves. Diagnosing and repairing faults is easy--dependent on the fault--but as software begins to drive the industry, surely it must be a question of big data?  
Without a doubt, the cars of the future will be able to diagnose more faults and conditions than ever before. That particular feature of car ownership may be dramatically influenced by the Internet of Things, and software controlled hardware could possibly outlive any useful life. That doesn't make vehicles self-repairing in the truest sense of the common meaning, but it does mean a huge step towards this goal.  
This paper proposes \"An Integrated Self-diagnosis System (ISS) for an Autonomous Vehicle based on an IoT Gateway and Deep Learning\" which collects information from the sensors of an autonomous vehicle, diagnoses itself and the influence between parts by using Deep Learning and informs drivers of the result. The ISS consists of 3 modules.  
The first In-Vehicle Gateway Module(In-VGM) collects the data from the in-vehicle sensors, the media data like a black box and a radar in driving, and the control message of a vehicle and transfers each data collected through each controller area network (CAN), FlexRay and Media Oriented Systems Transport (MOST) protocol to on-board diagnostics (OBD) or actuators. The data collected from the in-vehicle sensors is transferred to the CAN or FlexRay protocol and the media data in driving is transferred to the MOST protocol. Various types of messages transferred like this are transformed into a destination protocol message type. The second Optimized Deep Learning Module (ODLM) creates the Training Dataset on the basis of the data collected from the in-vehicle sensors and reasons the risk of the vehicle parts and consumables and the risk of the other parts influenced by a defective part. It diagnoses the risk of a vehicle's total condition. The third Data Processing Module (DPM), based on Edge Computing, has an Edge Computing based Self-diagnosis Service (ECSS) to improve the self-diagnosis speed and reduce the system overhead, while a V2X based Accident Notification Service (VANS) informs adjacent vehicles and infrastructures of the self-diagnosis result analyzed by the OBD.