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Advanced Sensing and Predictive Techniques in Intelligent Transportation Systems
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Advanced Sensing and Predictive Techniques in Intelligent Transportation Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 30 December 2026 | Viewed by 2300

Editor

Dr. Linheng Li

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 210018, China
Interests: transportation; traffic control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent Transportation Systems (ITSS) are undergoing a profound transformation driven by the rapid development of sensing technologies, connected infrastructures and data-driven intelligence. In modern transportation environments, heterogeneous sensors—such as cameras, LiDAR, radar, GNSS, wireless sensor networks, roadside units and embedded infrastructure sensors—are enabling increasingly detailed and real-time observation of vehicles, roads, users and surrounding environments. At the same time, the growing complexity of traffic systems requires not only accurate sensing of the current state but also the ability to anticipate near-future traffic evolution, driver behavior and potential risks. This shift from passive perception to proactive anticipation is becoming essential for safer, smarter and more resilient transportation systems. The topic is therefore highly timely, given the global push toward connected and automated mobility, smart cities and data-enabled traffic management.

This Special Issue aims to present and disseminate the most recent advances related to sensing, perception, prediction and anticipatory intelligence in Intelligent Transportation Systems. We consider contributions addressing novel sensing devices, multimodal sensor fusion, traffic state estimation, trajectory prediction, behavior anticipation, risk prediction, infrastructure-based sensing, cooperative perception and sensing-enabled transportation control. Original research articles and comprehensive review papers are both welcome. Particular interest will be given to contributions that bridge sensing technologies with predictive modeling and real-world ITS applications, thereby advancing both methodological innovation and practical deployment in transportation systems.

Topics of interest include, but are not limited to, the following:

  • Advanced sensors and sensing architectures for ITS;
  • Cooperative perception in connected and automated transportation systems;
  • Traffic state estimation, forecasting and digital sensing;
  • Vehicle trajectory prediction and behavior anticipation;
  • Sensing-based risk detection, safety assessment and anomaly prediction;
  • Edge/cloud sensing frameworks for smart mobility;
  • Infrastructure sensing, vehicle–road collaboration and V2X-enabled applications;
  • AI, machine learning and deep learning for transportation sensing and prediction.

Dr. Linheng Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent transportation systems
  • transportation sensing
  • sensor fusion
  • traffic state prediction
  • trajectory prediction
  • behavior anticipation
  • cooperative perception

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Published Papers (4 papers)

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Research

22 pages, 4690 KB  
Article
A Human-Centered Multimodal Framework for Characterizing Safety-Relevant Driver Functional Domains: An Exploratory Study of Professional Bus Drivers
by Ting-An Kuo, Chiuhsiang Joe Lin and Po-Hsiang Liu
Sensors 2026, 26(12), 3664; https://doi.org/10.3390/s26123664 - 8 Jun 2026
Viewed by 311
Abstract
This study proposes a human-centered multimodal framework for characterizing safety-relevant driver functional domains in professional bus drivers. Unlike conventional approaches that rely on isolated psychological or physical assessments, the proposed framework integrates self-perception, psychomotor performance, and cognitive–perceptual assessment to provide an exploratory, structured [...] Read more.
This study proposes a human-centered multimodal framework for characterizing safety-relevant driver functional domains in professional bus drivers. Unlike conventional approaches that rely on isolated psychological or physical assessments, the proposed framework integrates self-perception, psychomotor performance, and cognitive–perceptual assessment to provide an exploratory, structured characterization of driver-related functional capacities. Eighteen professional bus drivers participated in this study. Self-perception data were obtained from all 18 participants, whereas psychomotor and cognitive–perceptual assessments were completed by 16 participants. These measurements were used to examine multiple domains relevant to driving safety, including behavioral awareness, motor coordination, attention, visual tracking, and hazard-perception-related processing. Given the modest sample size, the study should be regarded as an exploratory pilot investigation. Data were analyzed using a laboratory-based cross-sectional between-subjects design to examine age- and gender-related differences across the assessed domains. The findings suggested that selected age- and gender-related differences and descriptive tendencies were observable across multiple domains. Male drivers descriptively showed higher self-rating scores, female drivers showed different performance tendencies in selected psychomotor tasks, and male drivers demonstrated substantially greater grip strength. Older drivers showed slower and less efficient performance in several cognitive–perceptual measures, with the clearest age-related effect observed in the tachistoscopic traffic test, where older participants showed a higher error tendency under time-constrained traffic-scene processing conditions. The constructs and measures proposed in this study are intended as general laboratory-based assessments of driver-related capabilities rather than direct measures of actual driving performance, real-time driver-state indicators, or validated sensor-based monitoring indicators. As candidate human-factor constructs, they may inform future driver monitoring research by helping clarify how driver-related signals or behaviors could eventually be linked to underlying functional and safety-related meaning in intelligent transportation environments. Full article
(This article belongs to the Special Issue Advanced Sensing and Predictive Techniques in Intelligent Transportation Systems)
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24 pages, 5394 KB  
Article
Traffic State Lane-Level Estimation Based on Transformer and Vehicle Trajectory Data
by Wei Bai, Yan Zhao, Yanni Ju, Jing Gan and Linheng Li
Sensors 2026, 26(11), 3376; https://doi.org/10.3390/s26113376 - 26 May 2026
Viewed by 355
Abstract
Investigating the fundamental link between microscopic vehicular motion parameters and macroscopic traffic flow states is pivotal for advancing refined traffic state estimation research and propelling the progression of Intelligent Transportation Systems. In this paper, a basic Transformer model has been optimized and extended [...] Read more.
Investigating the fundamental link between microscopic vehicular motion parameters and macroscopic traffic flow states is pivotal for advancing refined traffic state estimation research and propelling the progression of Intelligent Transportation Systems. In this paper, a basic Transformer model has been optimized and extended by incorporating embedding and pooling layers, and the model’s hyperparameters have been finely tuned through random search cross-validation. The creation of the Generalized Optimized Transformer (GOT) model ensued, where the multi-head attention mechanism adeptly encapsulates all spatiotemporal dynamics inherent in traffic data. Various benchmark models such as LSTM, RNN, and Transformer were put to test, each demonstrating unique performances in managing different traffic flow states. Among them, the GOT model exhibited superior performance, adeptly handling lane-level traffic state estimation tasks derived from microscopic vehicle trajectory data. In conclusion, this research elucidates the intricate and mutable mapping relationship between microscopic vehicular motion parameters and traffic flow states, proficiently executing lane-level traffic state estimation grounded on microscopic trajectory data. This paper is anticipated to provide fresh insights into the understanding of the complex relationship between microscopic vehicular motion parameters and traffic flow states. Full article
(This article belongs to the Special Issue Advanced Sensing and Predictive Techniques in Intelligent Transportation Systems)
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18 pages, 4887 KB  
Article
Enhancing Expressway Traffic State Perception: A Novel BAS-Optimized PSO-BP Fusion Model with Tensor Completion
by Jiacheng Yin, Xiaofei Guo, Wei Bai, Lijing Ma and Li Tang
Sensors 2026, 26(10), 2998; https://doi.org/10.3390/s26102998 - 10 May 2026
Viewed by 395
Abstract
With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial–temporal coverage and accuracy, which can hardly support the refined operation and management of intelligent expressways. Existing data preprocessing methods [...] Read more.
With the continuous expansion of the expressway network and the rapid growth of traffic demand, traditional single-source traffic detection data is limited in spatial–temporal coverage and accuracy, which can hardly support the refined operation and management of intelligent expressways. Existing data preprocessing methods often fail to fully capture global spatiotemporal features, and traditional PSO-BP neural networks are prone to local optima. To address these issues, this study investigates multi-source traffic data fusion using ETC-DSRC and RTMS microwave data from the Jiangsu section of the G50 Shanghai-Chongqing Expressway. The HaLRTC tensor completion algorithm is adopted to repair missing and abnormal data, fully mining the spatial–temporal correlation characteristics of traffic flow. The beetle antennae search (BAS) mechanism is introduced into the particle swarm optimization (PSO) process to improve particle search behavior and population diversity. On this basis, a BAS-optimized PSO-BP neural network, referred to as BSO-BP in this study, is constructed for multi-source traffic data fusion. In this model, the improved PSO algorithm is used to optimize the initial weights and thresholds of the backpropagation (BP) neural network, thereby improving the global search capability and convergence stability of the fusion model. Taking the average road speed as the fusion target, MAE, RMSE and MAPE are used for accuracy verification. The results show that the proposed model has significantly higher accuracy than single-source data methods and BP, PSO-BP, and GA-PSO-BP models, and can reflect the real traffic state of road sections more accurately. Full article
(This article belongs to the Special Issue Advanced Sensing and Predictive Techniques in Intelligent Transportation Systems)
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38 pages, 1509 KB  
Article
Relational Modelling for Automotive Cybersecurity: Structural Transition and Graph-Topology-Based CAN Intrusion Detection
by Mohammad Khalaf Khreasat and Gabriel Villarrubia González
Sensors 2026, 26(10), 2964; https://doi.org/10.3390/s26102964 - 8 May 2026
Viewed by 883
Abstract
A central open question in automotive intrusion detection is not merely whether relational representations of Controller Area Network (CAN) traffic improve performance, but which aspects of CAN traffic structure transfer robustly across attacks and which do not transfer across vehicle platforms, and why. [...] Read more.
A central open question in automotive intrusion detection is not merely whether relational representations of Controller Area Network (CAN) traffic improve performance, but which aspects of CAN traffic structure transfer robustly across attacks and which do not transfer across vehicle platforms, and why. To investigate this question systematically, we develop a lightweight intrusion-detection framework combining statistical traffic descriptors, structural identifier transition features, and graph topology representations extracted from sliding windows of CAN frames. Because CAN is a broadcast-only bus with no request–response mechanism, each ECU independently transmits its identifiers at fixed periodic rates; accordingly, the structural and graph-based features capture the temporal scheduling regularity of identifier broadcasts, not directed inter-ECU communication dependencies. Stress-testing the framework under cross-attack and cross-dataset transfer reveals a clear four-level hierarchy: (1) statistical features collapse under cross-attack transfer (ROC-AUC as low as 0.009), failing to generalise beyond the attack type seen during training; (2) structural transition features are the most robust form of representation, maintaining high cross-attack performance (ROC-AUC > 0.999) across all evaluated scenarios within the same vehicle platform; (3) graph topology features are scenario-dependent, achieving high robustness in DoS-trained scenarios but producing sub-random results in Fuzzy-trained scenarios, exposing a sensitivity to injection density profiles; and (4) the hybrid combination provides the strongest overall operational package, consistently across four classifiers. Cross-dataset transfer to the ROAD dataset reveals the precise boundary conditions of transferability: structural representations transfer only when an attack perturbs identifier transition regularity (correlated signal attacks, ROC-AUC = 0.81–0.83), while attacks that affect only payload semantics (speedometer) or exploit identifier–space novelty (fuzzing) lie outside the detection scope of transition-based features, regardless of the vehicle platform. A vehicle-specific calibration experiment further shows that the correlated-attack generalization gap can be closed with as little as 10% of target-vehicle normal traffic, whereas speedometer attacks remain structurally invisible by design. A key contribution of this work is therefore a transparent approach for identifying when relational CAN representations transfer and when they do not—a finding that is more scientifically valuable than a uniformly optimistic performance claim and which provides concrete guidance for practitioners designing cross-platform automotive IDS. Full article
(This article belongs to the Special Issue Advanced Sensing and Predictive Techniques in Intelligent Transportation Systems)
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