The spiking neural network (SNN) is the third generation of neural networks, valued for its biological interpretability, hardware compatibility, and low power consumption. While field programmable gate array (FPGA), based SNN accelerators show promise with their intelligent design and low power consumption, existing models lack a crucial spiking encoding module. This leads to increased pre-processing time on the CPU. To address these issues, this paper proposes an FPGA-based SNN accelerator with a spiking encoding module. This innovation reduces CPU pre-processing time by threefold without adding FPGA processing time. Achieving 92.62% accuracy on the MNIST dataset using LIF spiking neurons at 0.675 W, the accelerator outperforms CPU and GPU, being 14× faster and 2.5× faster, respectively, and more power-efficient with minimal resource usage. Given its pixel input, it can be seamlessly embedded in real-time classification systems for efficient classification and detection.

Quantum computing, a field utilizing the principles of quantum mechanics, promises great advancements across various industries. This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems, exploring its potential to transform areas such as traffic optimization, logistics, routing, and autonomous vehicles. By examining current research efforts, challenges, and future directions, this survey aims to provide a comprehensive overview of how quantum computing could affect the future of transportation.

Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier. However, its production, especially green hydrogen generated from renewable sources, is hindered by low efficiency and limited yield, primarily due to the performance of the catalysts used. Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes. For instance, screening for effective catalysts still heavily relies on human-lab-work, a process that is time-consuming. Facing this critical challenge, machine learning (ML) emerges as a promising solution. ML, a core component of data mining and analysis that uses statistical algorithms without explicit instructions, can rationalize the design of catalysts through the use of big data, including DFT results. This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies, offering a more effective path to uncovering vital methodologies for catalyst development. This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years. It presents an overview of the existing ML algorithms, exploring their specific functionalities, benefits, and limitations. Besides, this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production, particularly through electrochemical and biological processes.

Global warming is the most significant challenge to society due to the increasing carbon dioxide level in the atmosphere nowadays. Accelerating the deployment of zero-carbon fuel is imperative in the transportation field, one of the major sources of carbon emissions. The effort on clean energy represented by hydrogen has been made by showing good breakthroughs in some applications; however, many technical and economic challenges exist for large-scale commercial applications. Ammonia, as another zero-carbon fuel, is an efficient storage and transportation medium for hydrogen component and shows greater application potential. Compared to hydrogen, ammonia has more mature storage and transportation technologies, superior related infrastructure, and a further systematic transportation network. In the review, the current mainstream technology routes of ammonia synthesis, the storage and transportation, and the future development trends are summarized. Carbon emissions and costs for the processes are brought into focus. Additionally, the combustion and emission generation mechanisms with technical routes for ammonia as a direct fuel are discussed. The current opportunities and challenges are then analyzed for ammonia as a zero-carbon fuel. The prospect of utilizing ammonia both as a direct fuel and a carrier for hydrogen in transportation is being explored, along with an architecture of the zero-carbon energy system for transportation based on the ammonia fuel.

At the intersection of artificial intelligence and urban development, this paper unveils the pivotal role of Foundation Models (FMs) in revolutionizing Intelligent Transportation Systems (ITS). Against the backdrop of escalating urbanization and environmental concerns, we rigorously assess how FMs—spanning large language models, vision-language models, large multimodal models, etc.—can redefine urban mobility paradigms. Our discussion extends to the potential of modular, scalable models and strategic public-private partnerships in facilitating seamless integration. Through a comprehensive literature review and theoretical framework, this paper underscores the significant role of FMs in steering the future of transportation towards unprecedented levels of intelligence and responsiveness. The insights offered aim to guide policymakers, engineers, and researchers in the ethical and effective adoption of FMs, paving the way for a new era in transportation systems.

Hydrogen energy is a clean and versatile energy carrier, increasingly recognized for its role in a sustainable energy future due to its clean and abundant energy production. Bridging the gap between potential and practicality, digital Twin (DT) technology emerges as a pivotal artificial intelligence tool, providing a virtual modelling platform that enhances the operation and integration of hydrogen energy into modern energy systems. This review firstly explores the multifaceted applications of DT technology across different stages of the hydrogen energy lifecycle, including production, storage, transport, and utilization. It commences with a detailed introduction to DT technology, elucidating its definition, core principles, and structural nuances, thus laying the groundwork for understanding its pivotal role in energy systems. The core of the review delves into the applications of DT technology in hydrogen energy, segmenting the discussion into production, storage, transport, and utilization processes. Specific focus is given to optimizing fuel cells and hybrid electric vehicles through DT models, along with the seamless integration of hydrogen systems with broader energy networks. It further dissects the working mechanism of DT, highlighting the key features that contribute to itsgrowing prominence in the energy sector.

This research proposes a predictive lane-changing control system for platoon based on cloud control system (CPPLC), Which is designed to improve the safety, economy, and driving efficiency of a platoon. The system constructs a vehicle-cloud hierarchical control architecture, with the cloud as the decision-making layer, which collaboratively optimizes the longitudinal acceleration and lateral lane-changing decisions of the platoon based on a model predictive control framework to improve the comprehensive performance of platoon driving. The vehicle is the execution layer, which cooperates with the decision-making in the cloud to generate the platoon driving trajectory and carry out tracking control to ensure the safety of platoon driving. The proposed system is evaluated based on a joint simulation platform consisting of Sumo, Matlab/Simulink, and Trucksim, and the results show that the system can realize the improvement of the economy and driving efficiency while ensuring the safety compared with the conventional microscopic driving model.

China has established a high-speed railway network that has the longest operating mileage, the fastest operating speed, the most advanced technical system, and the most abundant application scenarios in the world. Even under high operating speed and complex environmental conditions, China's high-speed railway still maintains a high level of comfort, which has attracted extensive attention around the world. This paper selects six index factors that significantly affect the comfort of high-speed trains: running stability, transient pressure, electromagnetic environment, interior noise, braking, and longitudinal impulse, and curve centrifugal force. The factors affecting each index are analyzed. Different evaluation indexes and methods adopted by major countries in the world are compared. The riding comfort is evaluated from multiple angles. Moreover, the research trends in improving riding comfort are also reviewed.

Intelligent connected vehicles, as the focus of the global automotive industry, are currently at a critical stage of large-scale commercialization. However, during the development process of vehicles from mechanical systems with limited functions to mobile intelligence with complex and multiple functions, the issues of functional safety, cybersecurity, and safety of the intended functionality are the main challenges of the industrialization of intelligent connected vehicles, including multiple safety risks such as hardware and software failures, insufficient performance in edge scenarios, cyber-attacks and data leakage. In this paper, the safety and security issues of intelligent connected vehicles, the challenges posed by emerging technology applications, and related solutions are systematically reviewed and summarized. A fusion safety system framework with the safety cube as the core of protection and control is proposed innovatively based on a field-vehicle-human safety interactional model, realizing stereoscopic, deep, and comprehensive safety protection through end-cloud collaboration. Meanwhile, an X-shaped fusion safety development process based on CHAIN is proposed. Through the empowerment of digital twin and AI technologies, it could approach interaction between physical entities and digital twin models and the automation of the development process, thereby satisfying the demands of fusion safety system design, intelligent development, rapid delivery, and continuous iteration. The fusion safety system framework and X-shaped development process proposed in this paper can provide important insight into intelligent transportation vehicles and systems' safety and security design and development.