Additive manufacturing (AM) or 3D printing is a promising industrial technology that enables rapid prototyping of complex configurations. Powder Bed Fusion (PBF) is one of the most popular AM techniques for metallic materials. Until today, only a few metals and alloys are available for AM, e.g., titanium alloys, the most common of which is Ti-6Al-4V. After optimization of PBF parameters, with or without post processing such as heat treatment or hot isostatic pressing, the printed titanium alloy can easily reach tensile strengths of over 1100 MPa due to the quick cooling of the AM process. However, attributed to the unique features of metallurgical defects and microstructure introduced by this AM process, their fatigue strength has been low, often less than 30% of the tensile strength, especially in very-high-cycle regimes, i.e., failure life beyond 10⁷ cycles. Here, based on our group’s research on the very-high-cycle fatigue (VHCF) of additively manufactured (AMed) Ti-6Al-4V alloys, we have refined the basic quantities of porosity, metallurgical defects, and the AMed microstructure, summarized the main factors limiting their VHCF strengths, and suggested possible ways to improve VHCF performance.

The in-vehicle communication provides promising opportunities to improve the road safety and traffic efficiency. Previous studies demonstrated that the professional drivers have better driving skills than the non-professional drivers who allocate more attention to secondary tasks. However, they may not be sensitive to the new in-vehicle technology. In addition, these qualitative studies failed to elaborate on the visual and response behavior differences among different driver groups (professional drivers such as taxi, bus, motorcoach, and non-professional drivers), and lacked the quantitative analysis of driving patterns in a new environment. This paper explores the differences in visual interaction, response characteristics, driving performance, and behavior patterns between the professional and non-professional drivers in the connected environment through a case study of intersection-approaching behavior using a driving simulator. More precisely, two driving scenarios (baseline and human–machine interface (HMI)) were designed in the driving simulator, and 65 participants, including 34 professional drivers and 31 non-professional drivers, completed the experiment. In the HMI scenario, the driver was provided with the signal light phase and phase transition remaining time of the current intersection. This paper also proposes a driving pattern extraction model based on the Bayesian non-parametric method combined with a text clustering algorithm to perform a quantitative description of the driving patterns. The results show that the professional drivers tend to interact less with the HMI compared with the non-professional drivers. Moreover, the professional drivers’ first gaze at the HMI occurs and responds earlier. The proposed driving model can effectively describe 7 patterns of intersection-approaching behavior. The connected information can significantly improve the efficiency of the intersection traffic and the driving behavior. However, the professional drivers are more responsive and behave more consistently. This study can provide insights into the development of personalized assisted driving systems, as the two driving populations differ in their interactions, responses, and behavioral patterns.

Vehicle-to-Infrastructure (V2I) communication has provided a solution for the improvement of the traffic efficiency of smart city intersections. For example, turning maneuvers prediction at signalized intersections in a connected environment helps traffic command centers time traffic lights and dynamically predict traffic flow. However, the modeling methods used in existing research on this topic have some limitations, such as poor scalability and interpretability of machine learning. Thus, this study proposes a dictionary learning-based approach to predict turning maneuvers before the intersection. The proposed dictionary model estimates the LogDet divergence-based sparse inverse covariance matrix (LDbSICM) of driving behavior samples. The graphical lasso method is used to estimate the sparse inverse covariance matrix of the driving samples to construct a dictionary library of the maneuver behavior. The LogDet divergence is used to calculate the difference between each inverse covariance matrix. A driving simulator is utilized to collect experimental data consisting of turning left (TL), turning right (TR), and going straight (GS) behaviors to establish and evaluate the proposed model. The experimental results demonstrate that the proposed dictionary learning-based turning maneuver prediction model achieves 100% prediction accuracy for TL and GS and 97.2% for TR. The proposed model has substantial advantages over existing methods. The model can predict TL, TR, and GS in a connected environment 270, 280, and 290 m, respectively, before the intersection.

The records generated by taxicabs which are equipped with GPS devices is of vital importance for studying human mobility behavior, however we are focusing on taxi drivers' operation patterns in this paper. We identify a group of valuable characteristics, which are simple but effective, through large scale drivers' behavior in a complex metropolis environment. Based on the daily operations of 31,000 taxi drivers in New York City which covers over 14 million of trip records during Jan 1 to Jan 31 in 2013, we classify drivers into top, ordinary and low income groups according to monthly working load, daily income and daily ranking. Then, we apply big data analysis and visualization methods to compare the different characteristics among top, ordinary and low income drivers in selecting of working time, working area as well as driving routes. The results verify that top drivers do have special operation tactics to help themselves serve more passengers, travel faster thus make more money per unit time. This research provides new possibilities for fully utilizing the information obtained from urban taxicab data for estimating human behavior, which is not only very useful for individual taxicab driver but also to those policy-makers in city authorities.