# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 2 Digital Twin in Transportation Systems  
With the advancements made in Big Data, IoT, and AI, a new generation of information, technology, geographic data, and global positioning data, is to be handled. Combining these technologies with DT technology is critical in digital wave trends and takes the lead in transportation applications for planning, maintenance, security, and other aspects. The DT concept has the potential to improve the transportation sector by providing a digital identity, synchronized visualization, and virtual and real interactions (see Figure 3). DT technology utilizes intelligent technical advantages, such as controlling traffic perception, road warnings, and emergency responses. Furthermore, it can provide transportation solutions and new paths, such as intelligent driving, which will increase efficiency and safety and allow convenient traffic management. In this context, [PERSON] et al. [13] introduce a DT framework for connected vehicles using an advanced driver assistance system (ADAS). They used vehicle-to-cloud communication to calculate the advisory speed based on the information that could be collected from the sensors on the vehicles. The proposed model helps the driver to control the speed intelligently. Another example of using DT in the cloud was presented by [PERSON] and [PERSON] [14], who developed a DT model for the cloud-based cyber-physical system (C2 PS). They described the key properties of the C2 PS and introduced a telematics-based prototype driving assistance application for the vehicular C2 PS. In this context, a review of DT technology with smart EVs was conducted by [PERSON] et al. [15]. The review divided the smart vehicle systems into different categories: autonomous navigation control, advanced driver assistance systems, vehicle power electronics, vehicle health monitoring, BMS, and electric power drive systems. As a result of this research, smart EVs and DT technology were investigated theoretically to see what impacts their integration could have in the near future. Figure 2: Visualization of the geographical distribution of the analyzed articles. Due to the amount of data generated by transportation systems, machine learning (ML) and deep learning (DL) technologies are employed to create an ITS. The application of intelligence in the transportation field is increasing rapidly and improving the performances of these systems has become the research focus. The existing transportation systems can be controlled, analyzed, and operated using the DT concept. Using ML and DL in the DT concept can provide real-time data and effective services to the service provider and end-user [9]. Moreover, the ITS can effectively optimize and coordinate traffic conditions based on DL and DT technologies by monitoring the flow of people, traffic, and roads. It can also maximize the duration of traffic lights and find the signal light scheme with the shortest transit time. For example, [PERSON] et al. [16] proposed a DL algorithm to solve the security problems of the ITS. The proposed model assured a response time to emergency alerts and increased prediction accuracy. Moreover, vehicles will travel faster because they can better adapt to the road environment, transmit data faster, and develop routes by considering traffic patterns. DT and AI technologies are utilized in transportation via traffic management, prediction, and congestion avoidance. [PERSON] et al. [17] introduced an ITS that uses ML, fog/edge analytics, data lakes, DT, and blockchain. The authors used cameras to collect environmental information and then ran edge analytics on the collected data. The DT generated the virtual car model to simulate the real-world scenario. This work used ML and DL algorithms to predict driver intentions. By creating a virtual vehicle model, non-autonomous drivers were able to make better decisions depending on the current traffic scenario and the intents of other drivers. The DT can also improve the driving experience for travelers by reducing and redistributing wait times at intersections.