Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

In the intelligent transportation system (ITS), speed prediction plays a significant role in supporting vehicle routing and traffic guidance. Recently, a considerable amount of research has been devoted to a single-level (e.g., traffic or vehicle) prediction. However, a systematic review of speed prediction in and between different levels is still missing. In this article, existing research is comprehensively analyzed and divided into three levels, i.e. macro traffic, micro vehicles, and meso lane. In addition, this article summarizes the influencing factors and reviews the prediction methods based on how those methods utilize the available information to meet the challenges of the prediction at different levels. This is followed by a summary of evaluation metrics, public datasets, and open-source codes. Finally, future directions in this field are discussed to inspire and guide readers. This article aims to draw a complete picture of speed prediction and promote the development of ITS.

Cooperative vehicle-infrastructure system (CVIS) is an important part of the intelligent transport system (ITS). Autonomous vehicles have the potential to improve safety, efficiency, and energy saving through CVIS. Although a few CVIS studies have been conducted in the transportation field recently, a comprehensive analysis of CVIS is necessary, especially about how CVIS is applied in autonomous vehicles. In this paper, we overview the relevant architectures and components of CVIS. After that, state-of-the-art research and applications of CVIS in autonomous vehicles are reviewed from the perspective of improving vehicle safety, efficiency, and energy saving, including scenarios such as straight road segments, intersections, ramps, etc. In addition, the datasets and simulators used in CVIS-related studies are summarized. Finally, challenges and future directions are discussed to promote the development of CVIS and provide inspiration and reference for researchers in the field of ITS.