IoT-driven intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient, safe, smart, reliable, and sustainable. The IoT provides the access and driving forces of seamlessly integrating transportation systems from the physical world to the virtual counterparts in the cyber world. In this paper, we present visions and works on integrating the artificial intelligent transportation systems and the real intelligent transportation systems to create and enhance ``intelligence'' of IoT-enabled ITS. With the increasing ubiquitous and deep sensing capacity of IoT-enabled ITS, we can quickly create artificial transportation systems equivalent to physical transportation systems in computers, and thus have parallel intelligent transportation systems, i.e. the real intelligent transportation systems and artificial intelligent transportation systems. The evolution process of transportation system is studied in the view of the parallel world. We can use a large number of long-term iterative simulation to predict and analyze the expected results of operations. Thus, truly effective and smart ITS can be planned, designed, built, operated and used. The foundation of the parallel intelligent transportation systems is based on the ACP theory, which is composed of artificial societies, computational experiments, and parallel execution. We also present some case studies to demonstrate the effectiveness of parallel transportation systems.

In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee on Transportation 5.0 with the mission of promoting and transforming the deployment of advanced and innovative technologies, especially Artificial Intelligence in transportation. This paper briefly summarizes our main research and findings over the last decade. Transportation Foundation Models, Transportation Scenarios Engineering, and Transportation Operating Systems have been identified as the main directions for the research and development of next-generation intelligent transportation systems.

Autonomous driving (AD), including single-vehicle intelligent AD and vehicle–infrastructure cooperative AD, has become a current research hot spot in academia and industry, and multi-sensor fusion is a fundamental task for AD system perception. However, the multi-sensor fusion process faces the problem of differences in the type and dimensionality of sensory data acquired using different sensors (cameras, lidar, millimeter-wave radar, and so on) as well as differences in the performance of environmental perception caused by using different fusion strategies. In this article, we study multiple papers on multi-sensor fusion in the field of AD and address the problem that the category division in current multi-sensor fusion perception is not detailed and clear enough and is more subjective, which makes the classification strategies differ significantly among similar algorithms. We innovatively propose a multi-sensor fusion taxonomy, which divides the fusion perception classification strategies into two categories—symmetric fusion and asymmetric fusion—and seven subcategories of strategy combinations, such as data, features, and results. In addition, the reliability of current AD perception is limited by its insufficient environment perception capability and the robustness of data-driven methods in dealing with extreme situations (e.g., blind areas). This article also summarizes the innovative applications of multi-sensor fusion classification strategies in AD cooperative perception.

This paper is to discuss the state, trend, and frontiers of development of cyber-physical-social systems (CPSSs) in China. The demand for developing CPSS is discussed in detail, followed by the Artificial societies, Computational experiments, Parallel execution (ACP) approach for CPSS and knowledge automation. The development of ACP based on CPSS in transportation, energy, information, Internet of Things, and Internet of Minds (IoM) is discussed to demonstrate the cutting-edge applications in CPSS. Finally, the blockchainized IoM technology and the concepts of parallel society are described. This paper will contribute to the transition from the current social construct to a futuristic intelligent society.

Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.

The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels without knowing the information on identity, position, movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity, and dynamic information for the vessels of interest. However, the performance of AIS and video data fusion is susceptible to issues such as data spatial difference, message asynchronous transmission, visual object occlusion, etc. In this work, we propose a deep learning-based simple online and real-time vessel data fusion method (termed DeepSORVF). We first extract the AIS-and video-based vessel trajectories, and then propose an asynchronous trajectory matching method to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS-and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results under occlusion conditions. To validate the efficacy of our DeepSORVF, we have also constructed a new benchmark dataset (termed FVessel) for vessel detection, tracking, and data fusion. It consists of many videos and the corresponding AIS data collected in various weather conditions and locations. The experimental results have demonstrated that our method is capable of guaranteeing high-reliable data fusion and anti-occlusion vessel tracking. The DeepSORVF code and FVessel dataset are publicly available at https://github.com/gy65896/DeepSORVF and https://github.com/gy65896/FVessel, respectively.