Short Description
The recent development of the Internet of Things (IoT) brings forward numerous novel technologies whose application scenarios are not only limited to the user level (e.g., individual consumer or private company) but can also be applied to the system level (e.g., commercial or industrial sector). For example, the IoT plays a significant role in the current Intelligent Transportation System (ITS), which is a system consisting of vehicular communications, cloud computing, intelligent control, massive data management, and many other elements. By leveraging the IoT, different entities (e.g., vehicles, drivers, riders, infrastructures, traffic management centers, etc) in the existing transportation system get connected with each other, thus making the entire system smarter, safer, and more efficient.
A rising and ubiquitous trend in this IoT context is represented by the “digital twin”, where a real-time update of big data from the physical world’s entities is required to update the corresponding digital replicas in the cyber world. As an extension concept to the digital twin, “parallel driving” also considers the mental world besides the physical world and the cyber world, which models the cognitive behaviors of human drivers, with the ability to enable learning and interaction between the physical and cyber drivers. Both the computing architecture and the communication networks/protocols within the framework of digital twin or parallel driving are built to achieve higher efficiency, fidelity, and reliability.
However, these developments also bring significant challenges for authorities, industry, as well as scientific communities. In terms of system design and control, current IoT applications in ITS need to be refined or even redesigned to better function under uncertainties in demand, and to better cooperate with existing conventional vehicles and infrastructures. From the performance assessment perspective, models and simulation tools based on artificial intelligence and big data have been widely developed to verify the performance of IoT applications, in particular taking into account the increasing trends in-vehicle connectivity and automation. However, the validity of these models needs to be re-examined with field implementations.
This technical committee focuses on sharing the state-of-the-art design, models, algorithms, simulation, and field implementation of a wide range of IoT applications in ITS (such as digital twin and parallel driving), and identifies challenges as well as research needs, aiming to encourage cross-disciplinary cooperation. The main topics of interest to ITS-in-IOT TC include the following:
- Digital twin of intelligent vehicles
- Software-defined vehicles
- Vehicular cyber-physical systems (VCPS)
- Cyber security of connected and automated vehicles
- System design and field implementations of the Internet of Vehicles (IoV)
- Parallel driving/transportation
- Cloud computing and edge computing for connected vehicles
- Remote driving
- Artificial intelligence and big data application in urban mobility
- Modeling and simulation tools for network computing and communication
Committee Members List
- Qi Alfred Chen, University of California at Irvine, USA
- Yiran Chen, Duke University, USA
- Xuan Sharon Di, Columbia University, USA
- Lu Feng, University of Virginia, USA
- Kyungtae Han, Toyota Motor North America, USA
- Jia Hu, Tongji University, China
- Ruimin Ke, University of Texas at El Paso, USA
- BaekGyu Kim, Daegu Institute of Science and Technology, South Korea
- Lingxi Li, Indiana University-Purdue University Indianapolis, USA
- Chung-Wei Lin, National Taiwan University, Taiwan
- Shaoshan Liu, Perceptin, USA
- Chen Lv, Nanyang Technological University, Singapore
- Jibonananda Sanyal, Oak Ridge National Lab, USA
- Chris Schwarz, University of Iowa, USA
- Weisong Shi, University of Delaware, USA
- Guoyuan Wu, University of California at Riverside, USA
- Bowen Zheng, Pony.ai, USA
- Xuesong Zhou, Arizona State University, USA
