Merging at highway on-ramps while interacting with other human-driven vehicles is challenging for autonomous vehicles (AVs). An efficient route to this challenge requires exploring and exploiting knowledge of the interaction process from demonstrations by humans. However, it is unclear what information (or environmental states) is utilized by the human driver to guide their behavior throughout the whole merging process. This paper provides quantitative analysis and evaluation of the merging behavior at highway on-ramps with congested traffic in a volume of time and space. Two types of social interaction scenarios are considered based on the social preferences of surrounding vehicles: courteous and rude . The significant levels of environmental states for characterizing the interactive merging process are empirically analyzed based on the real-world INTERACTION dataset. Experimental results reveal two fundamental mechanisms in the merging process: 1) Human drivers select different states to make sequential decisions at different moments of task execution; and 2) the social preference of surrounding vehicles can impact variable selection for making decisions. It implies that efficient decision-making design should filter out irrelevant information while considering social preference to achieve comparable human-level performance. These essential findings shed light on developing new decision-making approaches for AVs.

Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

Pedestrian detection is an important branch of computer vision, and it has important applications in the fields of autonomous driving, artificial intelligence and video surveillance.With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and achieves better performance. However, the performance of state-of-the-art methods is far behind the expectation, especially when occlusion and scale variance exist. Therefore, a lot of works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. Firstly, brief progress of pedestrian detection in the past two decades is summarized. Secondly, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trend of pedestrian detection is prospected.

In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.

The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

Objective: To introduce patient-centered approach in China and to relate it with Chinese patient satisfaction via validating the Chinese version of Patient-Professional Interaction Questionnaire (PPIQ-C).Design: This cross-sectional survey was conducted through face-to-face interviews from June to September in 2019. Participants rated their patient-centered care experience via the 16-item translated PPIQ, their experience of the received medical service, and their overall satisfaction.Setting: Kunshan Huaqiao People's Hospital in Jiangsu, China.Participants: A total of 230 participants (87 males and 143 females; 108 outpatients and 122 inpatients).Results: PPIQ-C exhibited acceptable psychometric properties. Data revealed a single factor model of the 16 PPIQ-C items [χ(4)2 = 12.394, p = 0.823, CFI = 1.000, TLI = 1.019, RMSEA = 0.000, SRMR = 0.032] had a superior model fit over the original first-order with four correlated factors and the second-order structures. The overall reliability was excellent (McDonald's ω = 0.975). In terms of patient satisfaction, process, treatment quality, and communication significantly predicted patient satisfaction, while environment, staff attitude, and medical ethics did not [R² = 0.427, F₍₆₎ = 24.887, p < 0.001]. Most importantly, the total score of PPIQ-C predicted patient satisfaction above and beyond the above-mentioned medical service perspectives (B = 0.595, SE = 0.207, p = 0.004). Finally, the constructive effect of PCC on patient satisfaction was stronger for departments of Pediatrics than Surgery.Conclusions: The Chinese version of the PPIQ scale (PPIQ-C) exhibited acceptable psychometric properties. Yet the distinction among the four factors was not supported, suggesting potential difference(s) across cultures. Patient-centered care (PCC), reflected by the overall PPIQ-C score, predicted overall patient satisfaction above and beyond other medical service perspectives. Adopting PCC approach in appropriate situations will probably advance the development of performance evaluation systems in China, thus improving the overall health care and patient satisfaction.