## 2 Related Works
### Fault Diagnosis of a Vehicle  
Currently, most self-diagnostics for vehicles use non-OEM or OEM-based OBD methods [20; 21; 22]. However, they do not use any neural network model. In case of a neural network used for a vehicle, studies are underway on electric vehicles and connected vehicles. Besides, there is active research in various fields. Some of these studies diagnose the failure of a particular system in a vehicle through a deep-learning method. So we briefly describe those studies. The following are examples about studies that use neural network in vehicles. The sensor abrupt-faults of Hybrid Electric Vehicles (HEV) control system are focused. A novel method for diagnosing sensor abrupt-faults is proposed based on the Wavelet Packet Neural Network (WPNN). It puts the feature data that have been normalized as the input vector of the WPNN and trains the neural network. Then the features of a fault signal can be effectively identified [23]. A new data compression approach is provided and validated on a method based on Neural Network (NN) to detect both failure type and degree in a driving system. A brief method was introduced to preprocess training data by analyzing the linear relationship between features and patterns to be classified. This indicates that the proposed method used to preprocess data can significantly improve the efficiency and precision in categorizing all the fault samples, especially for the fault degrees considered in this study [24]. An Artificial Neural Network (ANN)-based fault diagnosis method after extraction of a new pattern is proposed. ANN is a neural network that sets various features of the converter as parameters. The new pattern of an AC-DC converter failure in view of SHEV application has been used as the training data of the ANN [25]. The diagnosis method based on a wavelet and neural network is proposed. This method does not need to collect a large number of data, but simplifies the diagnostic process while ensuring the accuracy of the diagnostic result. [PERSON] focuses on the Self-Organizing feature Map (SOM) neural network to diagnose the stator winding faults in the Permanent Magnet Synchronous Motor (PMSM). In order to train and work easily about the network, the fault feature data was extracted from the current sample by using the Wavelet Transform (see Figure 2). This method uses only stator currents as raw data; the energy of high frequency in the current is extracted based on the wavelet transform and then the energy is used as the fault feature after the normalizing processing [26]. [PERSON] examines an approach to increasing the safety of a vehicle's design with the help of ANNs integrated in the vehicle's self-diagnostic system. The method that is proposed to improve the safety of the vehicle's structural design based on artificial neural networks that are integrated in the self-diagnostic system, and these help to ensure a fast response due to the parallel processing of the flows of diagnostic information from the different nodes, assembly units, and systems, as well as increasing the credibility of the diagnostic state of the electronic systems. This also includes processing of individual information about the previous states of the systems of some specific vehicles. However, this suggestion is theoretical and there is no experimental data [27]. In order to avoid the interference and coupling of the control channels between Active Front Steering (AFS) and Active Suspension Subsystems (ASS), [PERSON] presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems [28]. [PERSON] proposes a novel vehicle detection scheme based on multi-task deep Convolutional Neural Networks (CNNs) and Region-Of-Interest (RoI) voting.