# Smart Mobility with Big Data: Approaches, Applications, and Challenges
## 4 Application of Smart Mobility with Big Data
### Autonomous Vehicles
In addition, the authors of [92] proposed an IoT gateway and deep learning-based self-driving car ISS (integrated self-diagnosis system), that collects information from sensors of autonomous vehicles and analyzes self-diagnosis and their effects. The proposed system uses deep learning to deliver information after self-diagnosis and manage the time when parts of autonomous vehicles can be safely replaced. Furthermore, in [93], a system was proposed to improve the control design of autonomous vehicles through big data analysis of signals measured in autonomous vehicles. The efficiency of the proposed control system was demonstrated through complex simulation examples using the CarSim simulation environment. In addition, the authors of [42] proposed an architecture that stores distributed data, processes streaming data for real-time analysis, and processes batch offline data to effectively utilize data collected by autonomous vehicles. The authors also described the data classification algorithm of distributed storage devices and mathematical modeling to analyze the data classification function of autonomous vehicles. The authors of [94] proposed an autonomous vehicle dynamics analysis focusing on side stability. The analysis was based on the collection of big data from vehicle signals. The key to the proposed analysis method is the C4.5 machine learning algorithm. The purpose of inspection is to analyze the relationship between the various signals (e.g., yaw rate, side slip angle, longitudinal speed, and adhesion coefficient) and the lateral dynamics of the vehicle. In addition, the authors of [95] proposed an OLAP-based (online analytical processing) analysis tool that utilizes big data to improve the reliability and efficiency of decision making for autonomous vehicles. The authors of [96] proposed an autonomous driving framework using deep reinforcement learning. This framework integrates the attention model to improve the time complexity by using glimpses and action networks to direct the CNN kernel to the input area associated with the driving process. Moreover, the authors of [97] proposed an application of the deep reinforcement learning technique to determine optimal driving policies by maximizing long-term rewards in interactive environments to solve complex control problems. The proposed architecture uses LSTM to model an interactive environment delivered to a DQN (Deep Q-Network), including historical driving information. In [98], a new probability recognition algorithm is presented as a real-time solution for data connection, object tracking, and object classification for autonomous ground vehicles under all weather conditions. The proposed method implements a state-of-the-art vision detection algorithm that includes directional information for autonomous vehicle applications. Moreover, in [99], unmanned autonomous vehicles were proposed with a focus on enhancing traffic sign recognition performance to assist in autonomous driving. Because not only color but also time constraints are important when recognizing traffic signs on a video basis, the authors proposed an algorithm to recognize traffic signs by fusing space and time functions with CNN bases. The authors of [100] proposed four rules for autonomous vehicles to prioritize ethical data exploitation and decision making between pedestrians and passengers.