The primary concern in the realm of intelligent vehicle (IV) technology is reducing collisions, particularly those involving vulnerable road users, such as pedestrians. Such incidents are detrimental to future mobility, as they deter people from walking, an active travel mode, and could be fatal. In this article, a collaborative intelligent transportation framework is presented to predict pedestrians’ intent using roadside closed-circuit television and edge computing devices with the support of vehicle-to-everything communication. We propose a contextual stacked ensemble approach that integrates an ensemble of ultralightweight feature-specific submodels, where each of the submodels focuses on body posture, traffic context, and relative location, respectively, aiming to enable accurate and real-time pedestrian intent prediction within the limited computational capacity of roadside edge devices. Experimental results demonstrate that the proposed approach outperforms existing state-of-the-art benchmarks and meets the time constraints on inference required by IVs.

Accurate and reliable vehicle positioning is a prerequisite for the development of cooperative intelligent transportation systems (C-ITSs), especially for automated driving applications and in scenarios where global navigation satellite systems (GNSSs) fail to meet the requirements of safety-critical services. This article presents an experimental assessment of a roadside infrastructure-based localization system that exploits a wideband wireless technology to estimate a vehicle’s position and communicates this information in real time to the vehicle via a vehicle-to-everything (V2X) link. We highlight the need to use advanced tracking methods to cope with heterogeneous vehicle dynamics. We validate the proposed system experimentally in both racetrack and highway tunnel scenarios using an ultra wideband (UWB) positioning network, showcasing the system’s feasibility through on-field implementations. The findings reveal that the wideband radio technology obtains positioning accuracy comparable to GNSS real-time kinematic with a mismatch lower than 30 cm in 95% of the cases. This capability provides a seamless high-accuracy positioning service and successfully unlocks autonomous navigation in tunnels. This article underscores the key role of the roadside infrastructure in enhancing autonomous vehicle navigation by integrating a precise positioning technology as UWB with V2X communication, thereby meeting the stringent requirements of safety-critical C-ITS applications.

The convergence of network, electronic, and control technologies has expanded the attack surface of industrial control systems (ICSs), necessitating the integration of functional safety and cybersecurity. Existing methods often treat safety and security as isolated domains, overlooking their interdependence in modern ICSs. This article proposes a novel methodology, Process-Oriented and Coalescent Analysis (POCA), to bridge this gap through an integrated safety and security analysis. Unlike previous approaches, POCA focuses on the intersection of cyberthreats and functional interactions, offering a new perspective on threat modeling. Applied to a railway signal system, a representative ICS that has received insufficient attention in cybersecurity research, POCA incorporates cyberattack scenarios, system service processes, and safety constraints to identify and assess risks. The analysis results highlight POCA’s effectiveness in uncovering vulnerabilities at the intersection of functional safety and cybersecurity, providing actionable insights for system design and operation.

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Accurate traffic forecasting is essential for intelligent transportation system management and control. Due to the highly complex spatiotemporal (ST) correlation of real-world road networks, dynamic and long-term traffic prediction presents many challenges. We propose a traffic speed prediction model based on dynamic structural prior (DSP) ST graph attention networks. We provide a structural prior graph, namely, dual graph convolution, which combines spatial and contextual subgraphs to enable the discovery of the non-Euclidean spatial correlation and potential contextual similarity of road networks. Moreover, to dynamically extract the ST correlation, this article employs a multihead self-attention temporal convolution module to capture the temporal correlation and a graph attention convolution module to extract the spatial correlation. The prediction output is generated by stacking multiple ST blocks. Experimental results on two real-world traffic datasets demonstrate that DSP-ST outperforms existing mainstream baselines, which can provide references for traffic management departments.