A new type of carbonized polymer dot was prepared by the one-step hydrothermal method of triethoxylsilane (TEOS) and citric acid (CA). The sensor made from carbonized polymer dots (CPDs) showed superior gas sensing performance toward ammonia at room temperature. The Si, O-codoped CPDs exhibited superior ammonia sensing performance at room temperature, including a low practical limit of detection (pLOD) of 1 ppm (Ra/Rg: 1.10, 1 ppm), short response/recovery time (30/36 s, 1 ppm), high humidity resistance (less than 5% undulation when changing relative humidity to 80 from 30%), high stability (less than 5% initial response undulation after 120 days), reliable repeatability, and high selectivity against other interferential gases. The gas sensing mechanism was investigated through control experiments and in situ FTIR, indicating that Si, O-codoping essentially improves the electron transfer capability of CPDs and synergistically dominates the superior ammonia sensing properties of the CPDs. This work presents a facile strategy for constructing novel high-performance, single-component carbonized polymer dots for gas sensing.@en

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.

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This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework's feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation.

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Implementing parts per billion-level nitric oxide (NO) sensing at room temperature (RT) is still in extreme demand for monitoring inflammatory respiratory diseases. Herein, we have prepared a kind of core-shell structural Hemin-based nanospheres (Abbr.: Hemin-nanospheres, defined as HNSs) with the core of amorphous Hemin and the shell of acetone-derived carbonized polymer, whose core-shell structure was verified by XPS with argon-ion etching. Then, the HNS-assembled reduced graphene oxide composite (defined as HNS-rGO) was prepared for RT NO sensing. The acetone-derived carbonized polymer shell not only assists the formation of amorphous Hemin core by disrupting their crystallization to release more Fe-N4 active sites, but provides protection to the core. Owing to the unique core-shell structure, the obtained HNS-rGO based sensor exhibited superior RT gas sensing properties toward NO, including a relatively higher response (Ra/Rg = 5.8, 20 ppm), a lower practical limit of detection (100 ppb), relatively reliable repeatability (over 6 cycles), excellent selectivity, and much higher long-term stability (less than a 5% decrease over 120 days). The sensing mechanism has also been proposed based on charge transfer theory. The superior gas sensing properties of HNS-rGO are ascribed to the more Fe-N4 active sites available under the amorphous state of the Hemin core and to the physical protection by the shell of acetone-derived carbonized polymer. This work presents a facile strategy of constructing a high-performance carbon-based core-shell nanostructure for gas sensing.

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It is a great challenge to develop efficient room-temperature sensing materials and sensors for nitric oxide (NO) gas, which is a biomarker molecule used in the monitoring of inflammatory respiratory diseases. Herein, Hemin (Fe (III)-protoporphyrin IX) is introduced into the nitrogen-doped reduced graphene oxide (N-rGO) to obtain a novel sensing material HNG-ethanol. Detailed XPS spectra and DFT calculations confirm the formation of carbon–iron bonds in HNG-ethanol during synthesis process, which act as electron transport channels from graphene to Hemin. Owing to this unique chemical structure, HNG-ethanol exhibits superior gas sensing properties toward NO gas (R_a/R_g = 3.05, 20 ppm) with a practical limit of detection (LOD) of 500 ppb and reliable repeatability (over 5 cycles). The HNG-ethanol sensor also possesses high selectivity against other exhaled gases, high humidity resistance, and stability (less than 3% decrease over 30 days). In addition, a deep understanding of the gas sensing mechanisms is proposed for the first time in this work, which is instructive to the community for fabricating sensing materials based on graphene-iron derivatives in the future.

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Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.@en