IEEE Transactions on Intelligent Transportation Systems

Predicting pedestrian crossing intentions is crucial for the navigation of mobile robots and intelligent vehicles. Although recent deep learning-based models have shown significant success in forecasting intentions, few consider incomplete observation under occlusion scenarios. To tackle this challenge, we propose an Occlusion-Aware Diffusion Model (ODM) that reconstructs occluded motion patterns…

Virtual simulation testing is crucial for ensuring automated vehicles safety, which offers low cost and good repeatability. The key is to test in various virtual driving scenarios, but often fails to strike a balance between scenario coverage and test efficiency. To address this issue, we propose a scenario generation method based on a Genetic Algorithm optimized Hamiltonian Monte Carlo sampling …

In the assisted driving-based Intelligent Transportation System (ADITS) featuring speed-guided functionality, intelligent vehicles are crucial for enhancing transportation sustainability. However, the impact of ADITS on transportation sustainability at different penetration rates remains unclear. This study combines road testing with simulation to address this gap. By integrating road test data, …

Modality imbalance is a significant challenge for multi-modal interaction at various depths in multispectral pedestrian detection under varying illumination environments. To overcome the limitations of current cross attention in addressing the modality imbalance, we propose the Cross-Modal Dual-Stream Feature Interaction Transformer (CDFIT). CDFIT capitalizes on the Transformer’s ability to learn…

Reinforcement learning (RL) is a promising approach for end-to-end autonomous driving, but its practical deployment remains challenging due to low sample efficiency and sensitivity to reward design. To address these challenges, this study presents a novel Q-advantage integrated human-guided reinforcement learning (QIHG-RL) framework that effectively combines the strengths of machine learning and …

Understanding the fine-grained trajectories of metro passengers, especially at the train and route levels, is essential for analyzing system-level dynamics and individual behavior. However, existing approaches often rely on strong behavioral priors or simplified boarding assumptions, limiting their generality and realism. This study proposes a fully data-driven framework for passenger trajectory …

Summary form only: Abstracts of articles presented in this issue of the publication.

In the 6G-enabled intelligent transportation systems (ITS), each intelligent transportation terminal needs to perform long-distance, low-latency image interaction to ensure real-time information exchange, including real-time vehicular environmental images and various vehicular media images. However, due to high computational cost and large computing resource usage, many learning-driven image comp…

The growing popularity of electric vehicles (EVs) has rendered public EV charging stations (EVCSs) vital for alleviating range anxiety and supporting long-distance travel. However, recent studies reveal security vulnerabilities in EVs and EVCSs against attacks. This paper addresses these security concerns by introducing injection attacks on the front-end Vehicle-to-Grid (V2G) communication using …

Modern navigation systems prioritize fuel efficiency and time savings, often overlooking the critical aspect of road safety. This study addresses this gap by developing an advanced safe route guidance approach for Electronic Route Guidance Systems that incorporates personalized safety metrics based on individual driving behaviors and road conditions. Using the Safety Performance Function to evalu…