Help-seeking behavior for erectile dysfunction： clinic-based survey in China

The behavior of Chinese patients seeking help for erectile dysfunction （ED） has not been described in detail. This was an observational study conducted using an outpatient clinic-based questionnaire survey of ED patients. From 2008 to 2009, physicians in 10 medical centers in China enrolled 2693 men （aged 25-70years） diagnosed with ED. The diagnosis was based on the International Index of Erectile Function 5 （IIEF-5） Questionnaire. The men completed a survey that asked questions about demographics, marital status, education level and household income as well as help-seeking behavior and awareness of medical therapy. The mean age of the 2693 men was 43.4 5.3years; 73% were 〈50-years-old and 49% had a high household income. The mean time between noticing ED and taking the first treatment was 4.3 2.1months. Of the 2577 respondents, physicians （54%） and the internet （52%） were most frequently consulted sources for information about ED. Young ED patients preferred using the internet and older patients preferred consulting with physicians. Western medicine （19%） and traditional Chinese medicine （16%） were most frequently used for treatment. Young ED patients preferred to first search the internet for information, whereas older patients first asked physicians for help. Side effects of treatment were the greatest concern, especially for older patients. Physicians and the internet are frequently consulted for ED information and therapy. On the basis of these survey results, we believe that physicians in China should enhance health education about ED, especially via the internet.

Human-Like Decision-Making of Autonomous Vehicles in Dynamic Traffic Scenarios

With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like autonomous driving is expected due to the impact of the differences between autonomous vehicles and human drivers on safety.Although human-like decision-making has become a research hotspot, a unified theory has not yet been formed, and there are significant differences in the implementation and performance of existing methods. This paper provides a comprehensive overview of human-like decision-making for autonomous vehicles. The following issues are discussed: 1) The intelligence level of most autonomous driving decision-making algorithms;2) The driving datasets and simulation platforms for testing and verifying human-like decision-making;3) The evaluation metrics of human-likeness;personalized driving;the application of decisionmaking in real traffic scenarios;and 4) The potential research direction of human-like driving. These research results are significant for creating interpretable human-like driving models and applying them in dynamic traffic scenarios. In the future, the combination of intuitive logical reasoning and hierarchical structure will be an important topic for further research. It is expected to meet the needs of human-like driving.

Reinforcement Learning Behavioral Control for Nonlinear Autonomous System

Behavior-based autonomous systems rely on human intelligence to resolve multi-mission conflicts by designing mission priority rules and nonlinear controllers.In this work,a novel twolayer reinforcement learning behavioral control(RLBC)method is proposed to reduce such dependence by trial-and-error learning.Specifically,in the upper layer,a reinforcement learning mission supervisor(RLMS)is designed to learn the optimal mission priority.Compared with existing mission supervisors,the RLMS improves the dynamic performance of mission priority adjustment by maximizing cumulative rewards and reducing hardware storage demand when using neural networks.In the lower layer,a reinforcement learning controller(RLC)is designed to learn the optimal control policy.Compared with existing behavioral controllers,the RLC reduces the control cost of mission priority adjustment by balancing control performance and consumption.All error signals are proved to be semi-globally uniformly ultimately bounded(SGUUB).Simulation results show that the number of mission priority adjustment and the control cost are significantly reduced compared to some existing mission supervisors and behavioral controllers,respectively.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

The Intellectualized Architecture of the Autonomous Micro- Mobile Robot Based- Behavior

Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the group behavior evolution simulating the human's learning and evolution, the autonomous micro mobile robot will automatically generate the suited actions satisfied the environment. However, the designer only devises some basic behaviors, which decreases the workload of the designer and cognitive deficiency of the robot to the environment. The results of simulation have shown that the architecture endows micro robot with the ability of learning, adaptation and robustness, also with the ability of accomplishing the given task.

Tourist behavior patterns in the ecotourism destination in the west of China: A case study of Gannan Tibetan Autonomous Prefecture

This study takes Gannan Tibetan Autonomous Prefecture as the place of case study and tourists as research objects. From the perspectives of geographical distribution of source tourist markets, Tourist activity behavioral and spatial patterns of Tourists, this study looks into the geographical structure of the source tourists and spatial patterns by geography. The analysis of 341 questionnaires on tourists shows that:(1) The tourism cycle of Gannan is in the development phase, competing with adjacent Aba, and greatly impacted by the substitution effect and shadow effect of Aba.(2) The spatial distribution of tourist sources is concentrated, indicating that Gannan is a regional tourism destination. The temporal distance of tourists is mainly concentrated within the 6-hour traffi c circle.(3) Gannan Tibetan Autonomous Prefecture has already become the composite tourist destination dominated by leisure vacation. The minority folkcustom and special landscape are the most attractive tourism resources. Due to the impact of man-land harmonious lifestyle in the tourist areas, the environmental attitude of tourists is improved, and the transportation and shopping are the most vulnerable links in tourism service in Gannan Tibetan Autonomous Prefecture.(4) The spatial behavior of tourists in Gannan is mainly of single-destination style(52%), Transit leg and circle tour style(7%) as well as circle tour style(41%). The spatial distribution of tourist fl ow in Gannan shows a signifi cant feature "more in the north, less in the south and dependent on National Road". Tourism resources, transport facilities, regional competition and lack of route connecting different ecological units are important causes of the spatial distribution of self-help tourists.

A Structural Analysis of Senior High School Students’English Achievement Goal Orientation,Learning Anxiety,and Autonomous Learning Behavior

The present study aimed to investigate senior high school students to explore the relationships among their English achievement goal orientations,learning anxiety,and autonomous learning behavior.748 first-year senior high school students in Guizhou Province,China were selected as participants.A comprehensive questionnaire measuring the above variables was designed to collect the data.The Structural Equation Modeling(SEM)was used to analyze the data.The results showed that the model had good fit to the sample.The students’mastery goals and performance-approach goals positively contributed to their autonomous learning behavior,whereas their performance-avoidance goals were negatively associated with their autonomous learning behavior.The students’mastery goals effectively reduced their learning anxiety,but their performance-approach goals and performance-avoidance goals engendered learning anxiety.The students’learning anxiety and their autonomous learning behavior were negatively correlated.

Factors That Promote Autonomous and Controlled Motivation in Self-management Behavior of Hemodialysis Patients

This study examined the differences and primary factors from the impact of autonomous motivation and controlled motivation on the self-management behavior of hemodialysis patients.Anonymous,self-describing questionnaires were used for research on nine different dialysis facilities of 413 people who regularly visit.From using the primary factor results of multiple regression analysis,that took autonomous motivation and controlled motivation as the dependent variable,a path diagram was created that led to each motivation.The acknowledgement of autonomy support facilitated whether it was autonomous motivation or controlled motivation(The standardized coefficient was 0.385,0.346,p<0.0001).Positive evaluation coping skills were a primary factor that promoted autonomous motivation,while trait anxiety,disorders of social activities,and lack of motivation were primary factors that promoted controlled motivation.In order to raise the autonomous motivation to promote self-management behavior in patients with hemodialysis treatment,situations that easily cause amotivation and anxiety,as well as tendencies for depression should be assessed.Also the encouragement to attain positive evaluation coping skills to support patient autonomy appears to be effective.

Human Body Behavior as Response on Autonomous Maneuvers, Based on ATD and Human Model

In the near future, active safety systems will take more control over the vehicle driving, even up to introducing fully autonomous vehicles. Nowadays, it is expected that the active safety systems will aid avoiding collisions much more efficiently than human drivers. These systems can protect not only the passengers, but also other road users. To mitigate collision, certain maneuvers （e.g., sudden braking, lane change, etc.） need to be done in a reasonably quick time. However, this may lead to low-g energy pulses. The latter fact, may cause unexpected and, in some cases, unwanted occupant body motion resulting even in OOP （out of position） postures. New patterns of occupant reactions in such cases are, to some extent, confirmed experimentally [1-3]. This paper evaluates the limits of standard ATDs （anthropometric test devices） and chosen human models in well established maneuver scenarios. Obtained results are compared with experimental data available in the literature. Drawbacks identify new challenges for the near future simulation based safety engineering. One scenario with combined conditions of emergency braking during lane change has been used as an example of OOP posture after maneuver.

Modeling and gender difference analysis of acceptance of autonomous driving technology

In order to deeply analyze the differences in the acceptance of autonomous driving technology among different gender groups,a multiple indicators and multiple causes model was constructed by integrating a technology acceptance model and theory of planned behavior to comprehensively reveal the gender differences in the influence mechanisms of subjective and objective factors.The analysis is based on data collected from Chinese urban residents.Among objective factors,age has a significant negative impact on women's perceived behavior control and a significant positive impact on perceived ease of use.Education has a significant positive impact on men's perceived behavior control,and has a strong positive impact on women's perceived usefulness(PU).For men,income and education are found to have strong positive impacts on perceived behavior control.Among subjective factors,perceived ease of use(PEU)has the greatest influence on women's behavior intention,and it is the only influential factor for women's intention to use autonomous driving technology,with an influence coefficient of 0.72.The influencing path of men's intention to use autonomous driving technology is more complex.It is not only directly affected by the significant and positive joint effects of attitude and PU,but also indirectly affected by perceived behavior controls,subjective norms,and PEU.

基于深度强化学习的无信号灯路口决策研究

无信号灯左转路口是自动驾驶场景中最为危险的场景之一,如何实现高效安全的左转决策是自动驾驶领域的重大难题。深度强化学习(DRL)算法在自动驾驶决策领域具有广阔应用前景。但是,深度强化学习在自动驾驶场景中存在样本效率低、奖励函数设计困难等问题。提出一种基于专家先验的深度强化学习算法(CBAMBC SAC)来解决上述问题。首先,利用SMARTS仿真平台获得专家先验知识;然后,使用通道-空间注意力机制(CBAM)改进行为克隆(BC)方法,在专家先验知识的基础上预训练模仿专家策略;最后,使用模仿专家策略指导深度强化学习算法的学习过程,并在无信号灯路口左转决策中进行验证。实验结果表明,基于专家先验的DRL算法比传统的DRL算法更具优势,不仅可以免去人为设置奖励函数的工作量,而且可以显著提高样本效率从而获得更优性能。在无信号灯路口左转场景下,CBAM-BC SAC算法与传统DRL算法(SAC)、基于传统行为克隆的DRL算法(BC SAC)相比,平均通行成功率分别提高了14.2和2.2个百分点。

基于尾灯灯语的混行交通流车辆驾驶意图识别模型研究

针对长期存在自动驾驶车辆(autonomous vehicle,AV)与人工驾驶车辆(human-driven vehicle,HV)混行的交通现状,结合深度学习和HSV颜色特征提取方法,提出了一种在混行交通环境下基于尾灯灯语的车辆驾驶意图识别模型。以Mask R-CNN(mask region proposal convolutional neural network)车辆模型检测出的感兴趣区域RoI(region of interest)为限制,将区域内的HV作为研究对象,根据尾灯位置相关性,在HV车尾区域添加纵横向约束来定位传递灯语信号的左右尾灯;在规定的灯语组合及转向灯闪烁频率基础上,提出了一种基于时间序列的灯语识别算法,运用多目标判别相关性滤波CSRT(channel and spatial relatability tracking)跟踪HV尾灯并统计尾灯时序状态;以动态灯语作为输入,构建基于尾灯灯语的驾驶意图识别模型;使用基于真实路况信息的Cityspaces数据集和交通流视频数据对模型进行训练、验证和测试。研究结果表明:基于尾灯灯语的驾驶意图识别模型对视频流车辆尾灯检测准确率和召回率分别为96.0%、98.2%,对驾驶意图识别的平均准确率达到了95.9%,单帧识别耗时为20 ms,为高速混行环境下的AV决策规划提供了有效的理论依据。

不同感知群体对自动驾驶汽车购买和使用意向的研究

为探讨L4级自动驾驶汽车(AV)的出行体验对受访者购买和使用意向的影响,受访者被分为体验感知较高和较低群体,在体验前和体验后,利用多项Logit模型建立不同体验感知群体AV购买和使用意向模型。研究结果显示,购买和使用AV的想法不确定程度对体验前、后的选择均产生显著影响,对于体验感知较高群体,体验后该变量的影响程度有所下降,而对于体验感知较低群体则有所增加。AV的价格与购买和使用意向之间呈现较强的正相关关系,价格的升高会增加购买和使用想法的不确定性,从而导致受访者放弃购买AV,选择使用社会共享自动驾驶汽车(SAV)。对于体验感知较高群体来说,体验后对AV行驶过程的感知提升会促进其购买并自己使用AV,而通过提高对AV出行感受和出行安全的感知,将促进他们购买AV并有偿共享给他人使用。另外影响体验感知较低群体对AV购买和使用意向的显著因素数量则明显减少。

基于图卷积交互网络的车辆轨迹预测方法

精确预测邻近车辆的未来轨迹对自动驾驶汽车的决策和运动规划至关重要,现有研究倾向于利用递归神经网络(RNN)对车辆的时间交互进行建模,但其对车辆交互建模的可解释性差,忽略了实际的车道结构,在捕捉车辆与其环境的交互方面存在不足。为解决这一问题,本文提出了一种基于图卷积交互网络的考虑车道拓扑约束的车辆轨迹预测模型。其中车辆交互关系提取模块在构建车辆的空间关系时增加了边缘权重,以考虑车辆的邻近交互,使交互更具可解释性;行驶场景表征模块旨在通过从高精地图中提取车道拓扑来提高车辆轨迹预测的准确性;轨迹预测模块将上述两个模块的输出进行集成,并输出预测的未来轨迹。这种集成允许对道路结构和车辆行驶轨迹之间的相互作用进行更精确的建模。实验结果表明,与主流方法相比,该模型在Argoverse数据集上取得了良好的性能,提高了复杂道路结构下车辆轨迹规划的准确性和合理性。

基于安全裕度的网联自主汽车换道行为风险量化及动态平衡模型

伴随车联网技术的发展,道路交通流呈现智能网联自动驾驶汽车与传统人工驾驶车辆混合共存发展态势,研究网联新型混合车流换道驾驶行为的风险特性极其重要。基于安全裕度理论,建立了换道行为风险量化模型,采用故障树分析法,推导换道的时间和空间风险,进行时空融合的风险评定量化,以判断车辆是否处于安全变道状态,并动态平衡车辆换道行为可能存在的风险。运用SUMO软件对建立的量化模型进行仿真验证分析,碰撞时间倒数与瞬时风险系数均值分别下降0.1与0.05,同时变化趋势趋于稳定。安全裕度风险量化模型使换道风险得到了有效控制的同时,交通流的稳定性得到了较大提高,可保障未来网联环境中自主驾驶车辆队列的稳态运行,从而提高交通容量和交通效率。

交叉口车辆行为感知在线半监督混合方法

自动驾驶感知系统须对目标车辆运动进行感知,以制定合理交互决策。针对行为感知在时间上的滞后性和数据中可能存在的波动和异常值导致感知准确率差的问题,本文提出一种在线半监督混合方法。首先,采用自回归积分移动平均和在线梯度下降优化器设计基于数据驱动的车辆运动状态在线预测算法。然后,构建基于微簇的初始模型,并以K近邻为基分类器建立集成学习策略,设计错误驱动代表性学习和指数衰减策略实现对初始模型的迭代更新。最后,基于驾驶模拟平台采集了验证所提算法有效性的实验数据。结果表明,所提出的方法对于车辆行为波动具有快速适应性,在线预测算法可准确预测车辆运动趋势,行为感知算法对于不同预测时间下的车辆行为均有较强适应能力。

基于深度强化学习的智能车辆行为决策研究

自动驾驶车辆决策系统直接影响车辆综合行驶性能,是实现自动驾驶技术需要解决的关键难题之一。基于深度强化学习算法DDPG(deep deterministic policy gradient),针对此问题提出了一种端到端驾驶行为决策模型。首先,结合驾驶员模型选取自车、道路、干扰车辆等共64维度状态空间信息作为输入数据集对决策模型进行训练,决策模型输出合理的驾驶行为以及控制量,为解决训练测试中的奖励和控制量突变问题,改进DDPG决策模型对决策控制效果进行优化,并在TORCS(the open racing car simulator)平台进行仿真实验验证。结果表明:所提出的决策模型可以根据车辆和环境实时状态信息输出合理的驾驶行为以及控制量,与DDPG模型相比,改进的模型具有更好的控制精度,且车辆横向速度显著减小,车辆舒适性以及车辆稳定性明显改善。

考虑经验驾驶行为的入弯实时类人速度规划方法

为提高自动驾驶汽车的安全性、舒适性与通行效率,提出了一种考虑到滑行及安全速度的类人入弯速度规划策略。该策略基于混沌优化理论与实车弯道行驶速度数据,通过将车辆的入弯速度规划问题构建为多目标优化问题,建立了舒适模式与效率模式。通过定义奇点速度,简化了非线性高阶约束条件。结果表明:在该策略规划中,在不同弯道场景下,横向加速度和纵向加速度均在摩擦圆约束内,可保证车辆的行驶安全。相比于未考虑滑行的方法,在舒适模式下,该策略产生的纵向加速度减小9.76%,通行效率提高61.73%;在效率模式中,纵向加速度为加速度阈值,满足加速度约束,通行效率提高88%。因而,无论舒适模式还是效率模式,该方法均可兼顾舒适性和通行效率。

高职学生自主学习行为的发生机理及效能提升策略

学生学习能力是教育改革能否达到预期目标的重要决定因素。在学习型社会、技术技能型社会构建背景下,高职学生自主学习能力和习惯的培养是高职院校人才培养的重要课题。基于社会认知理论与交互决定论,以学习环境、学习策略作为变量,将学习动机作为中介变量,构建了高职学生自主行为影响因素理论关系模型。采用SPSS23.00和AMOS24.0软件对学习环境、学习动机、学习策略对自主学习行为的影响因素进行了分析,结果表明,学习环境和学习策略显著正向影响自主学习行为,学习动机则在学习环境对自主学习行为的影响中起到一定中介作用。基于此,可以通过打造适宜的学习环境、推动构建高效的学习策略、激发学生的学习动机来提高高职学生自主学习行为的效能。

青少年自主健身行为养成教育模式研究

青少年自主健身行为是青少年积极发展的重要表现,能够有效促进其身心社德行全面健康发展,提升美好生活满足感。充分考虑青少年自主健身行为养成教育过程中主体被制度约束、受环境促进、自主稳固的三阶段特征,构建以全面健康、积极发展为目标理念,以外部环境协同联动和个体“非自主--自主”螺旋式提升为内容体系,以全校性与全员性相结合、系统性与科学性相结合、长期性与反复性相结合、教育与自我教育相结合、因材施教与区别对待相结合、注重运动量-效关系为实施途径,以教学与实践保障、管理与反馈保障、研究与创新保障为实施保障的养成教育模式,旨在为青少年自主健身行为养成教育提供理论参鉴。