## 2 Related Works
### Fault Diagnosis of a Vehicle
In the design of the CNN architecture, this study enriches the supervised information with subcategory, region overlap, bounding-box regression, and a category for each training RoI as a multi-task learning framework [29]. [PERSON] proposes a method, which integrates deep neural networks with balanced sampling to address the challenges of classifying imbalanced data acquired from visual traffic surveillance sensors. The proposed method is able to enhance the mean precision of all the categories, in the condition of high overall accuracy [30]. A Lane Departure Prediction (LDP) method based on the Monte Carlo simulation and a Deep Fourier Neural Network (DFNN) is proposed to make improvements on the vision-based Lane Departure Warning Systems (LDWS). The proposed technique enhances the system's functions of the over-speed warning on a curved road and the over-steer warning on a low-adhesion road [31]. A probabilistic model named the Collision Prediction model based on a GA-optimized Neural Network (CPGNN) for decision-making in the rear-end collision avoidance system is proposed [32]. [PERSON] presents a novel method to detect vehicles using a far infrared automotive sensor. Firstly, vehicle candidates are generated using a constant threshold from the infrared frame. Contours are then generated by using a local adaptive threshold based on maximum distance, which decreases the number of processing regions for classification and reduces the false positive rate. Finally, vehicle candidates are verified using a Deep Belief Network (DBN) based classifier. This study is approximately a 2.5% improvement on previously reported methods and the false detection rate is also the lowest among them [33]. [PERSON] uses the process of real-time intrusion detection on a robotic vehicle. This study shows that by offloading this task to a remote server, we can utilize approaches of much greater complexity and detection strength based on deep learning [34]. A novel framework is proposed for vehicle Make and Model Recognition (MMR) using local tiled deep networks. A Local Tiled Convolutional Neural Network (LTCNN) is proposed to alter the weight sharing scheme of a CNN with a local tiled structure. This architecture provides the translational, rotational, and scale invariance as well as the locality. The experimental results show that their LTCNN framework achieved a 98% accuracy rate in terms of vehicle MMR [35]. [PERSON] investigates the possibility of exploiting deep neural features towards robust vehicle detection and a vehicle detection framework, which combines Deep Convolutional Neural Network (DCNN) based feature learning with an Exemplar-SVMs (E-SVMS) based, robust instance classifier to achieve robust vehicle detection in satellite images. Their models show that the combination of both schemes can benefit from each other to jointly improve the detection accuracy and effectiveness [36]. Figure 2: Process of diagnosis health and stator winding fault in Permanent Magnet Synchronous Motor (PMSM). [PERSON] firstly puts forward a new memetic evolutionary algorithm, named the Monkey King Evolutionary (MKE) Algorithm, for global optimization. It gives an application of this algorithm to solve the least gasoline consumption optimization (find the least gasoline consumption path) for vehicle navigation. They use this algorithm to find the least gasoline consumption path in vehicle navigation, and have conducted experiments that show the proposed algorithm outperforms A* algorithm and the Dijkstra algorithm as well [37]. [PERSON] proposes a CNN model of visual attention for image classification. Their systematic experiments on a surveillance-nature dataset that contains images captured by surveillance cameras in the front view demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks [38]. A vehicle fault detection and diagnosis combining Auto Associative Neural Networks (AANN) and several Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is proposed.