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Cooperative driving of human driver and automated system can effectively reduce the necessity of extremely accurate environment perception of highly automated vehicles, and enhance the robustness of decision-making and motion control. However, due to the two players’ different intentions, severe conflicts may exist during the cooperation, which often result in negative consequences on driving safety and maneuverability. This paper presents an indirect shared control method to model the situation and improve the driving performance, which focus on the affine input nonlinear vehicle dynamic system for shared controller design under the framework of non-zero sum differential game. The Nash equilibria strategy indicates the best response for the automated system, which can guide the automated controller to act more safely and comfortably. Aimed to obtain fast solution for practical application, approximate dynamic programming is utilized to find the Nash equilibria, which is represented by deep neural networks and solved iteratively. Driver-in-the-loop tests on a driving simulator were conducted to verify the performance of the proposed method under highway driving scenarios. The results show that the designed controller is able to reduce the driving workload and ensure the driving safety. [ABSTRACT FROM AUTHOR]

Due to the growing interest in automated driving, a deep understanding on the characteristics of human driving behavior is critical for human-like autonomous vehicles. Among various driving behaviors, lane change is the most important one for vehicle lateral driving safety. This study proposes an unsupervised method to extract and discover the behavioral patterns of lane change maneuvers for the purpose of exploring the composed behavioral patterns during lane change. This method involves two phases: Firstly, the lane change sequences will be segmented into blocks using time-series segmentation algorithms. Three segmentation algorithms were utilized in this study. In the second phase, the segments will be clustered to find the corresponding behavioral pattern of each segment. Two extended latent Dirichlet allocation (LDA) models were adopted to cluster the segments. The combination of different segmentation and clustering algorithms were evaluated and compared by employing entropy and perplexity as the evaluation criteria. Collected lane change data from naturalistic driving were applied to examine its effectiveness. The results show that this method could effectively mine descriptive behavioral patterns from lane change data. This study provides a promising data mining solution to facilitating deep and comprehensive understanding on driver lane change behaviors, which will promote the development of human-like autonomous vehicles. [ABSTRACT FROM AUTHOR]

Emergency Medical Services (EMS) are of great importance to saving people's lives from emergent accidents and diseases by efficiently picking up patients using ambulances. The transporting capability of an EMS system (e.g., defined as the average pickup time of patients) significantly depends on the real-time redeployment strategy of ambulances. That is, which station should an ambulance be redeployed to, after it becomes available (after it transports a patient to a hospital or after it finishes the in-site treatment for a patient)? However, it is a challenging task concerning with the multiple data D1-D5 as detailed in Introduction. To this end, in this paper, we propose a data-driven real-time ambulance redeployment approach that redeploys an ambulance to a proper station after it becomes available, so as to optimize the transporting capability of an EMS system, considering the aforementioned multiple data D1-D5. Specifically, the proposed approach is comprised of two stages to well consider the D1-D5. First, we propose a method (a safety time-based urgency index) to incorporate D1, D2, and D3 into each ambulance station's urgency degree (D*). Second, we propose an optimal matching algorithm to combine D*, D4, and D5 into the redeployment of the current available ambulance. Experimental results using data collected in real world demonstrate the significant advantages of our approach over many baselines. Comparing with baselines, our approach can save $\sim$ ∼ 4 minutes ($\sim$ ∼ 35 percent) of the average pickup time for each patient, improve the ratio of patients picked up within 10 minutes from 0.684 and 0.803 ($\sim$ ∼ 17 percent), and largely enhance the survival rate of patients ($\sim$ ∼ 12 percent for patients in category A1 and $\sim$ ∼ 17 percent for patients in A2). [ABSTRACT FROM AUTHOR]

Forecasting CNY exchange rate accurately is a challenging task due to its complex coupling nature, which includes market-level coupling from interactions with multiple financial markets, macrolevel coupling from interactions with economic fundamentals, and deep coupling from interactions of the two aforementioned kinds of couplings. This study develops a new deep coupled long short-term memory (LSTM) approach, namely, DC-LSTM, to capture the complex couplings for USD/CNY exchange rate forecasting. In this approach, a deep structure consisting of stacked LSTMs is built to model the complex couplings. The experimental results with 10 years data indicate that the proposed approach significantly outperforms seven other benchmarks. The DC-LSTM is verified to be a useful tool to make wise investment decisions through a profitability discussion. The purpose in this article is to clarify the importance of coupling learning for exchange rate forecasting, and the usefulness of deep coupled model to capture the couplings. [ABSTRACT FROM AUTHOR]

In this paper, a tapered optical fiber-based sensor is developed for the detection of uric acid (UA) in human serum. The fiber probes were fabricated using single-mode fiber (SMF) with taper diameter of $40~\mu \text{m}$. The diameter of taper section has culminated on the basis of excretion of evanescent waves and their handling capacity. To enhance the sensitivity, taper section (sensing region) was immobilized with synthesized gold nanoparticles (AuNPs). The AuNPs were used to initiate the excitation of surface plasmons (SP) at the fiber surface and ultimately leads to the localized surface plasmon resonance (LSPR) phenomenon. The comparative studies were also done to analyze the effect of size of AuNPs by immobilizing the sensor probes with ~10 nm and ~30 nm size of AuNPs. The UV-Vis spectrophotometry, high resolution-transmission electron microscopy (HR-TEM) and scanning electron microscopy (SEM) were used for the characterization of AuNPs solution and developed sensor probes. The sensing ability of sensors was authenticated by recording the transmitted intensity of LSPR spectrum for a wide range of UA from $10~\mu \text{M}$ to $800~\mu \text{M}$. The detected range of UA completely covers its concentration available in human serum that is from $150~\mu \text{M}$ to $420~\mu \text{M}$. Thereafter, the wavelength shift in LSPR spectrums was evaluated to determine the sensitivity and detection limit of sensors. The selectivity of the sensor probe was enhanced by functionalizing it with uricase enzyme (0.672 U/ml). The specificity of uricase functionalized sensor probes were also analyzed in the presence of other biomolecules found in human-serum. [ABSTRACT FROM AUTHOR]