Aims & Scope

The IEEE Open Journal of Intelligent Transportation Systems covers theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS), defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. Topics of consideration are:

    Big data analytics, machine learning, AI applications
    Communication (inter-vehicle, vehicle-to-roadside)
    Computers (hardware, software)
    Control (adaptive, fuzzy, cooperative, neuro)
    Decision Systems (expert systems, intelligent agents)
    Systems (engineering, architecture, evaluation)
    Information Systems (databases, data fusion, security)
    ITS Energy and Environment
    ITS, freight and logistics
    ITS in Developing Countries
    Human-Machine Interfaces
    Imaging & Real-Time Image Analysis
    Privacy & security of ITS
    Sensors (infrastructure and vehicle-based)
    Simulation (continuous, discrete, real-time)
    Signal Processing
    Smart Public Transport Systems
    Societal implications of ITS
    Standards
    Reliability & Quality Assurance
    Technology Forecasting & Transfer
    ​Traffic Flow Modelling and Control

Special Issue on Safety-oriented Perception Systems for Intelligent Vehicles and Transportation Systems
Submission Date: 2024-12-31

Intelligent and autonomous vehicles have gained significant traction in recent years. Intelligent vehicles have the potential to reduce transportation-related accidents and improve efficiency, making transportation safety a top priority. As one of the most important systems of intelligent vehicles, perception system provides the vehicle with vital information about its surroundings.

Perception systems enable the vehicle to perform functions such as localization, object detection, tracking, and trajectory
prediction, helping the vehicle determine its absolute position on the map and its relative position with other vehicles, and
allowing it to understand the presence of other traffic participants, which enables a wide range of applications, including
advanced driver assistance systems and autonomous driving. To generate a more comprehensive, reliable, and robust
perception of the vehicle's surroundings, multi-sensor fusion-based perception is gaining increasing attention from academia and industry. Sensors such as radar, LiDAR, cameras, GNSS, and other environmental sensors can significantly enhance the performance of the perception system. Additionally, leveraging Vehicle-to-Everything (V2X) techniques, cooperative perception enables even more potential applications. By enabling intelligent vehicles and road-side infrastructures to share information from various sensors through a cooperative network, each agent in the system can sense out-of-range surroundings and obtain more accurate and robust perception results. This cooperative approach enhances the safety of transportation systems, making them more reliable and efficient. However, there are still a lot of challenges for perception systems in real application, such as complicated driving conditions, sensor failure, signal delay, and so on. In this perspective, the objective of this special issue is to gather the recent advancing research and industrial implementation on both ego-vehicle perception systems and cooperative perception systems. The topics of interest include, but are not limited to:

➢ Object detection algorithms
➢ Object tracking algorithms
➢ Trajectory prediction algorithms
➢ Multi-sensor fusion integrated localization
➢ SLAM techniques
➢ Cooperative perception based on V2V, V2I and V2X
➢ Sensor fault detection and isolation for perception systems
➢ Multi-sensor calibration for perception systems
➢ Testing and evaluation for perception systems

Submission deadline: December 31, 2024.
Publishment: Papers will be published upon acceptance and available through early access on IEEE Xplore.
Papers can be submitted at: https://mc.manuscriptcentral.com/oj-its, choose submission type: Special Section

Guest editors:
Dr. Lu Xiong, Professor, Tongji University, China (xiong_lu@tongji.edu.cn)
Dr. Letian Gao, Postdoctoral Researcher, University of California, Los Angeles (UCLA), USA (letiangao@g.ucla.edu)
Dr. Xin Xia, Research Scientist, University of California, Los Angeles (UCLA), USA (x35xia@g.ucla.edu)
Dr. Chuchu Fan, Assistant Professor, Massachusetts Institute of Technology (MIT), USA (chuchu@mit.edu)
Dr. Rui Liu, Associate Professor, Chang’an University, China (ruiliu@chd.edu.cn)