为提高分布式驱动电动汽车在极限行驶工况下的稳定性,提出了一种具有三层结构的直接横摆力矩控制策略.顶层控制器解析驾驶人期望行驶状态值;上层控制器采用非奇异快速终端滑模控制(NFTSMC),决策维持车辆稳定行驶所需附加横摆力矩;下层控制器以车辆稳定裕度最大为目标,以电机输出极限、路面附着极限、轮胎纵/侧向力耦合关系为约束,基于加权最小二乘法实现4轮力矩动态优化分配.基于MATLAB/Simulink和Carsim仿真平台开展仿真试验.结果表明:相比滑模控制,车速为70 km/h时,双移线和正弦迟滞工况下,质心侧偏角最大跟踪误差分别减小66.7％、45.8％,均方根误差分别减小64.8％、56.4％,可见该策略能够提高期望状态跟踪精确性,改善车辆在极限行驶工况下的稳定性.

When exploring time series datasets, analysts often pose "which and when" questions. For example, with world life expectancy data over one hundred years, they may inquire about the top 10 countries in life expectancy and the time period when they achieved this status, or which countries have had longer life expectancy than Ireland and when. This paper proposes TimePool, a new visualization prototype, to address this need for univariate time series analysis. It allows users to construct interactive "which and when" queries and visually explore the results for insights.

Graphical Abstract Electrocatalytic carbon dioxide reduction (CO2 ECR) over metal phosphorous trichalcogenide (MPCh3) nanosheets was systematically investigated by Dong Wang, Chen Shen, Dan Ren, Hongguang Wang, and co-workers in their Research Article (e202217253). Unlike the layered CoPS3 and NiPS3 nanosheets, the active Sn atoms tend to be exposed on the surfaces of nonlayered SnPS3 nanosheets, which exhibit clearly improved formic acid selectivity.

Two-dimensional (2D) materials catalysts provide an atomic-scale view on a fascinating arena for understanding the mechanism of electrocatalytic carbon dioxide reduction (CO2 ECR). Here, we successfully exfoliated both layered and nonlayered ultra-thin metal phosphorous trichalcogenides (MPCh(3)) nanosheets via wet grinding exfoliation (WGE), and systematically investigated the mechanism of MPCh(3) as catalysts for CO2 ECR. Unlike the layered CoPS3 and NiPS3 nanosheets, the active Sn atoms tend to be exposed on the surfaces of nonlayered SnPS3 nanosheets. Correspondingly, the nonlayered SnPS3 nanosheets exhibit clearly improved catalytic activity, showing formic acid selectivity up to 31.6 % with -7.51 mA cm(-2) at -0.65 V vs. RHE. The enhanced catalytic performance can be attributed to the formation of HCOO* via the first proton-electron pair addition on the SnPS3 surface. These results provide a new avenue to understand the novel CO2 ECR mechanism of Sn-based and MPCh(3)-based catalysts.

The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi-RRT algorithm is proposed, which is suitable for obstacle avoidance of the vehicle in an unknown dynamic environment. The vehicle constraint considering the driver's driving habit and the obstacle-free direct connection mode of two random trees are introduced. Multi-sampling biased towards the target state reduces invalid searches, and parent node selection with the comprehensive measurement index accelerates the algorithm's execution while making the initial path gentle. The adaptive greedy step size, introducing the target direction, expands the node more effectively. Moreover, path reorganization minimizes redundant path points and makes the path's curvature continuous, and path coherence makes paths between the frames connect smoothly. Simulation analysis clarifies the efficient performance of the proposed algorithm, which can generate the smoothest path within the shortest time compared with the other four algorithms. Furthermore, the experiments on dynamic environments further show that the proposed algorithm can generate a differentiable coherence path, ensuring the ride comfort and stability of the vehicle.

The light spot on the ground at the portal of a tunnel, which is caused by the sun shining on a pergola, consists of bright areas and dark areas, and it can cause a discomfort glare for drivers. In this study, in order to evaluate the level of discomfort glare, the bright areas were compared to lamps and the dark areas were compared to the backgrounds. Using Unified Glare Rating (UGR), which is used to evaluate the glare degree in lighting places, we established a quantitative evaluation method of the discomfort glare and derived the formulas that could calculate the UGR of the pergola with equal beam spacing and unequal beam spacing. In accordance with the formulas, the UGR of the two kinds of pergolas was calculated. The results showed that the UGR of the pergola with equal beam spacing was smaller than the UGR of the pergola with unequal beam spacing, demonstrating a less level of the discomfort glare. Furthermore, the relationship between the variations of the parameters of the pergola and the UGR was analyzed, which could provide references for the design of the pergola and the improvement of driving comfort.

Electric bike (e-bike) riders’ inappropriate go-decision, yellow-light running (YLR), could lead to accidents at intersection during the signal change interval. Given the high YLR rate and casualties in accidents, this paper aims to investigate the factors influencing the e-bikers’ go-decision of running against the amber signal. Based on 297 cases who made stop-go decisions in the signal change interval, two analytical models, namely, a base logit model and a random parameter logit model, were established to estimate the effects of contributing factors associated with e-bikers’ YLR behaviours. Besides the well-known factors, we recommend adding approaching speed, critical crossing distance, and the number of acceleration rate changes as predictor factors for e-bikers’ YLR behaviours. The results illustrate that the e-bikers’ operational characteristics (i.e., approaching speed, critical crossing distance, and the number of acceleration rate change) and individuals’ characteristics (i.e., gender and age) are significant predictors for their YLR behaviours. Moreover, taking effects of unobserved heterogeneities associated with e-bikers into consideration, the proposed random parameter logit model outperforms the base logit model to predict e-bikers’ YLR behaviours. Providing remarkable perspectives on understanding e-bikers’ YLR behaviours, the predicting probability of e-bikers’ YLR violation could improve traffic safety under mixed traffic and fully autonomous driving condition in the future.

The mini-challenge 2 of VAST Challenge 2019 asks the participants to make sense of the radiation conditions in St. Himark using radiation readings from the both stationary monitors and mobile sensors, particularly, to detect and monitor a bunch of contaminated cars running in the city. This paper presents our visual analysis solution to detect and localize these cars using various visualization techniques, including small multiples, distribution histogram, and animation. As a result, we detected most of these cars, characterize their behavior during the given time period and give suspected locations of these cars at the end. We also developed a visual analysis interface using Tableau to help users evaluate the uncertainty of sensor readings, make sense of radiation changes in different area, and make future plans to deploy more sensors.

Serendipitous drug usage refers to the unexpected relief of comorbid diseases or symptoms when taking medication for a different known indication. Historically, serendipity has contributed significantly to identifying many new drug indications. If patient-reported serendipitous drug usage in social media could be computationally identified, it could help generate and validate drug-repositioning hypotheses. We investigated deep neural network models for mining serendipitous drug usage from social media. We used the word2vec algorithm to construct word-embedding features from drug reviews posted in a WebMD patient forum. We adapted and redesigned the convolutional neural network, long short-term memory network, and convolutional long short-term memory network by adding contextual information extracted from drug-review posts, information-filtering tools, medical ontology, and medical knowledge. We trained, tuned, and evaluated our models with a gold-standard dataset of 15714 sentences (447 [2.8%] describing serendipitous drug usage). Additionally, we compared our deep neural networks to support vector machine, random forest, and AdaBoost.M1 algorithms. Context information helped to reduce the false-positive rate of deep neural network models. If we used an extremely imbalanced dataset with limited instances of serendipitous drug usage, deep neural network models did not outperform other machine-learning models with n-gram and context features. However, deep neural network models could more effectively use word embedding in feature construction, an advantage that makes them worthy of further investigation. Finally, we implemented natural-language processing and machine-learning methods in a web-based application to help scientists and software developers mine social media for serendipitous drug usage.

Artificial monitoring remains to be a major way to detect anomalous events in expressway tunnels. To estimate the reliability of artificial monitoring on anomalous events in expressway tunnels, the video surveillance and mobile inspection based reliability models of artificial monitoring on the anomalous event in the expressway tunnel were built, and Monte Carlo method was applied to calculate the probability and mean time to detect the anomalous event at the specific time. The results showed that the Monte Carlo method could simulate video surveillance and mobile inspection, and obtain the probability distribution and mean time of detecting anomalous events. The mean time to spot the anomalous event was in reverse relation with the number of inspectors, the time of mobile inspection, and the reliability probability of the monitoring pre-warning system in tunnels and was in positive relationships with the departure interval. Combined with the actual operation cost, the model serves as a basis for the artificial monitoring package.

The objective of this study was to investigate how route familiarity affected drivers’ eye movement features (fixation and saccade) and driving speed when driving in the entrance zone of highway tunnels with different spatial visual conditions. On-road tests were conducted on the drivers’ visual characteristics and the speed were recorded in real time using an eye tracker and onboard diagnostic system. The variations in the eye movement features and speed in the entrance zone of the tunnels were analyzed. Then, statistical methods were conducted to examine the influence of the route familiarity and spatial visual conditions of tunnels on the driver behavior. The results demonstrated that the variations in the drivers’ eye movements and speed were much more significant in the entrance zone of a tunnel without spatial intervisibility than in a tunnel with spatial intervisibility. The impact of this environmental transition on unfamiliar drivers was greater than that on familiar drivers. Road familiarity reduced the drivers’ period of adaptation to the tunnel entrance environment and increased the driving speed.

Emergency evacuation is to transfer people from dangerous places to safe areas, so as to reduce or even avoid the potential harm to people.It is inherently a comprehensive system composed of evacuation managers, evacuees, road networks, shelters, etc. Security is one of the important indicators of such system.Moreover, in order to ensure the normal and efficient operation of evacuation system, each component should cooperate well with each other, thus making stability another important index of the evacuation system.In order to optimize evacuation safety, some residential areas may be arranged to stay much longer which is hard to be accepted, namely, the stability of evacuation system is low.In this paper, a system-based evacuation CSO model at residential level is proposed which compromises the security and stability of evacuation systems.The CSO model is a bi-level network optimization model, the upper level aims at minimizing the total risk of evacuation subject to the residential tolerance level and the lower level conveys a cell transmission-based dynamic traffic assignment problem.Using our model, we also study the impact of the number of shelters, the organizational form of road intersections, the uncertainty of evacuation demand and risk distribution on evacuation system.

The objective of the current study was to examine how experienced and inexperienced driver behaviour changed (including heart rate and longitudinal speeds) when approaching and exiting highway tunnels. Simultaneously, the NARX neural network was used to predict real-time speed with the heart rate regarded as the input variable. The results indicated that familiarity with the experimental route did decrease drivers' mental stress but resulted in higher speed. The proposed NARX model could predict synchronous speed with high accuracy. These results of the present study concern how to establish the automated driver model in the simulation environment.

In order to reduce the detecting time of expressway tunnel unexpected events and improve the efficiency of emergency response, the detecting time distribution regularity of the unexpected events under multiple manual monitoring patterns is investigated. First, the basic stochastic model of manual monitoring system of expressway tunnel unexpected events is established. Next, in consideration of the human reliability while performing the monitoring tasks, the model is modified while the correction coefficients characterizing the personnel's working status and working conditions and environment are proposed. Then, the solution method is raised based on Monte Carlo method. Finally, taking the actual monitoring system of an expressway extra-long tunnel in Shaanxi Province for example, the expected detecting time and its distribution regularity of the unexpected events are obtained by computation, and the expected detecting time and distributions under different monitoring and management patterns are compared. The result indicates that (1) the detecting probability of unexpected events increases with time prolonging after the events;(2) the expected detecting time and distribution difference between single monitoring and multiple monitoring is not significant, while the expected time for detecting the unexpected events can be shorten effectively from 1. 96 min with one monitor to 1. 17 min with multiple monitors inspecting screens separately, and the time that unexpected event can be guaranteed to be detected decreases sharply from 6. 6 min to 3. 0 min; ( 3 ) the worse the working condition and personnel's working status, the longer the expected detecting time will take, and the detecting time extends to 16. 8 min with the correction coefficients ranging from 3. 0 to 10. 0;(4) working environment and personnel's working status are of vital importance for detecting unexpected events. Finally, the meaning of the simulation result for event handling and emergency rescue work is discussed.

In order to investigate the changes of drivers state and its effects on the traffic flow under the condition of executing vehicle secondary tasks,first of all,based on adaptive control of thought-rational (ACT-R) cognitive structure and Distract-R software platform,four types of secondary tasks were established,and the distraction states of different drivers and the state of non-secondary tasks were simulated cognitively.Obtaining the ratio of the drivers' execution time and the distracting time during the performance of four types secondary tasks,and the driver distraction state database was established as the basic data for traffic flow simulation under secondary tasks conditions;then,based on the cellular automata traffic flow STCA model,the rules of deceleration were modified and the traffic flow simulation model considering the effects of vehicle secondary task was established.The joint simulation of cellular automata model and cognitive model was realized through model data exchange.Finally,cellular automata simulation was used to simulate the traffic flow situations of 0,10％,20％ drivers during executing vehicle secondary tasks when they are driving with the four task types randomly selected.Experimental data show that the joint simulation of cellular automata and cognitive model can reflect the psychological difference of drivers and variation characteristics of traffic flow;the model modifies the rules of original cellular automata deceleration,and parts of model parameters can be obtained by calling database of drivers' distraction states.Additionally,the vehicle secondary task causes obvious effects on the traffic flow to make the road maximize traffic capacity get reduced.The maximize traffic capacity reduced about 23.5％ and 40.7％ when 10％,20％ drivers executing secondary tasks,with the phenomena of increasing regional congestion and exacerbating queuing.Execution of vehicle secondary task not only causes the impact on the driving safety,but when drivers in the attention-distraction state reach a certain proportion,the overall state of the traffic flow is also significantly effected.1 tab,8 figs,27 refs.

为研究驾驶人行驶通过特长隧道环境中的心理负荷变化特性,选取2座典型特长隧道进行实车试验,采集驾驶人实时心电信号,以心率和心率变异性指标分析为基础,通过数据挖掘构建了基于因子分析的心理负荷计算模型,采用心率变异性频域分析结果对模型进行验证.研究结果表明:心率变异性指标在计算心理负荷时比心率指标具有更高的效度和信度,驾驶人在距离隧道入口较远处和距离隧道出口较近时负荷较大;在隧道路段和普通高速路段,熟悉试验道路的驾驶人平均心理负荷小于不熟悉试验道路的驾驶人;被试在隧道路段的平均心理负荷大小依次为入口段、出口段、行车段,熟练驾驶人心理负荷在特长隧道入口前300 m至前180 m范围受到的影响最为明显;非熟练驾驶人心理负荷在入口前300 m至入口后240 m范围受到的影响最为明显.上述结果说明:在隧道出、入口段,尤其是入口段驾驶人负荷过高,也是造成事故数量多的主要原因之一;熟悉道路条件可在一定程度上降低驾驶人心理负荷;普通高速路段虽然行车环境较好,但运行车速过高也会造成驾驶人负荷增加.熟练驾驶人心理负荷在隧道入口前升高,而非熟练驾驶人心理负荷在进入隧道后仍保持较高水平.

In order to investigate the influence of loading conditions and road conditions on safe driving speed threshold on curve section of road,oversize vehicle dynamics model,road scene model and driver con-trol model were established by using TruckSim software.Simulation tests were conducted in terms of the load-ing quality and road adhesion coefficient,and the contributory factors to vehicle rollover and sideslip on curve section of road and the degree of influence were analyzed.Finally,one curve section on an expressway in Shaanxi Province was taken as an example,and the safe driving speed threshold influenced by two factors was studied.The results indicate that the safe driving speed threshold on curve section decreases with increasing loading quality of freight,and it shows a power function relationship between safe driving speed threshold and loading quality;safe driving speed threshold on the curve section presents a downward trend while the road ad-hesion coefficient increases from 0.1 to 0.6,and the safe driving speed threshold and road adhesion coefficient show an exponential function relationship;safe driving speed threshold on the curve section remains stable while the road adhesion coefficient increases from 0.6 to 1 .1 .In accordance with the model simulation analy-sis results and aimed at overload and over-limited problems,this paper gives some security advice to traffic management departments,stevedores as well as drivers,which can improve the active safety for driving on the curve section of road and provide theoretical and technical support for road safety operation research.

In order to determine a suitable relative location of AHP and H point in the vehicle cab and improve the SAE standard design model, this paper modeled the H point which is suitable for Chinese according to human factor engineering principle. First, this paper optimized the location of H point by theoretical analyzing. It can be found that the dimension parameter of human body had an impact on H point location. Besides this, when compared this relationship with the SAE empirical formula, the reasonable ranges of vertical dimension between H point and AHP point are different. Then JACK was used to create Chinese male’s digital model models and their human percentile was set to 5, 50, and 95% which are representative. These models were used to simulate the location of AHP point and H point by evaluating, Comfort, Vision Zones, and Reach Zones. Least square method was used to process the location data and find the optimal H point. The result showed that the model to design H point for Chinese male in this paper was more reasonable than that in SAE. It can be used to design the driving cab for Chinese vehicles.