Simulating operational behaviors of pedestrian navigation

Navigation is an innate ability for humans, but simulating this capability in a virtual environment is no easy task and has been of interest to researchers for over a decade. This paper describes the development of ISAPT, an individual-based Intermodal Simulator for the Analysis of Pedestrian Traffic. ISAPT’s development is based on the observed behaviors of pedestrians reported from the literature and simulates the strategies employed by pedestrians for collision avoidance, including changes in speed and trajectory, passing strategies, and distance between objects. The implementation of these behaviors and strategies is described in the paper along with the results from a validation study. These results illustrate that the micro-level simulation of individual pedestrians gives ISAPT the ability to reproduce identified macro-level pedestrian behavior, as well as the capability to reproduce the operational statistics of an observed pedestrian corridor. Such functionality is necessary to support the use of simulation as a tool for designers and planners in the design and evaluation of intermodal facilities.     

A genetic fuzzy system to model pedestrian walking path in a built environment

A study on the pedestrian’s steering behaviour through a built environment in normal circumstances is presented in this paper. The study focuses on the relationship between the environment and the pedestrian’s walking trajectory. Owing to the ambiguity and vagueness of the relationship between the pedestrians and the surrounding environment, a genetic fuzzy system is proposed for modelling and simulation of the pedestrian’s walking trajectory confronting the environmental stimuli. We apply the genetic algorithm to search for the optimum membership function parameters of the fuzzy model. The proposed system receives the pedestrian’s perceived stimuli from the environment as the inputs, and provides the angular change of direction in each step as the output. The environmental stimuli are quantified using the Helbing social force model. Attractive and repulsive forces within the environment represent various environmental stimuli that influence the pedestrian’s walking trajectory at each point of the space. To evaluate the effectiveness of the proposed model, three experiments are conducted. The first experimental results are validated against real walking trajectories of participants within a corridor. The second and third experimental results are validated against simulated walking trajectories collected from the AnyLogic® software. Analysis and statistical measurement of the results indicate that the genetic fuzzy system with optimised membership functions produces more accurate and stable prediction of heterogeneous pedestrians’ walking trajectories than those from the original fuzzy model.     

Directional guidance from audible pedestrian signals for street crossing

Typical audible pedestrian signals indicate when the pedestrian walk interval is in effect but provide little, or even misleading information for directional alignment. In three experiments, blind and blindfolded sighted adults crossed a simulated crossing with recorded traffic noise to approximate street sounds. This was done to investigate how characteristics of signal presentation affected usefulness of the auditory signal for guiding crossing behaviour. Crossing was more accurate when signals came only from the far end of the crossing rather than the typical practice of presenting signals simultaneously from both ends. Alternating the signal between ends of the crossing was not helpful. Also, the customary practice of signalling two parallel crossings at the same time drew participants somewhat toward the opposite crossing. Providing a locator tone at the end of the crossing during the pedestrian clearance interval improved crossing accuracy. These findings provide a basis for designing audible pedestrian signals to enhance directional guidance. The principal findings were the same for blind and sighted participants and applied across a range of specific signals (e.g. chirps, clicks, voices).     

基于ANSYS的抗性消声器性能仿真分析

运用有限元分析软件ANSYS对内插管抗性消声器内部声场及流体进行了有限元分析。在讨论了管道声场流体运动方程的基础上，首先建立抗性消声器的流体有限元分析模型和声场有限元分析模型，以定性分析为目标，通过加载、求解以及后处理等一系列的步骤对消声器内部声场及流体进行计算分析，获得消声器内部不同频率下的声压及流体速度和压力分布情况。通过分析结果可以方便直观地获取消声器内插管中心偏移对消声器插入损失及流体流速和压力的影响，分析结果为消声器的优化设计提供可靠的依据。     

Recognition of pedestrian trajectories and attributes with computer vision and deep learning techniques

Analyzing the walking behavior of the public is vital for revealing the need for infrastructure design in a local neighborhood, supporting human-centric urban area development. Traditional walking behavior analysis practices relying on manual on-street surveys to collect pedestrian flow data are labor-intensive and tedious. On the contrary, automated video analytics using surveillance cameras based on computer vision and deep learning techniques appears more effective in generating pedestrian flow statistics. Nevertheless, most existing methods of pedestrian tracking and attribute recognition suffer from several challenging conditions, such as inter-person occlusion and appearance variations, which leads to ambiguous identities and hence inaccurate pedestrian flow statistics.Therefore, this paper proposes a more robust methodology of pedestrian tracking and attribute recognition, facilitating the analysis of pedestrian walking behavior. Specific limitations of a current state-of-the-art method are inferred, based on which several improvement strategies are proposed: 1) incorporating high-level pedestrian attributes to enhance pedestrian tracking, 2) a similarity measure integrating multiple cues for identity matching, and 3) a probation mechanism for more robust identity matching. From our evaluation using two public benchmark datasets, the developed strategies notably enhance the robustness of pedestrian tracking against the challenging conditions mentioned above. Subsequently, the outputs of trajectories and attributes are aggregated into fine-grained pedestrian flow statistics among different pedestrian groups. Overall, our developed framework can support a more comprehensive and reliable decision-making for human-centric planning and design in different urban areas. The framework is also applicable to exploiting pedestrian movement patterns in different scenes for analyses such as urban walkability evaluation. Moreover, the developed mechanisms are generalizable to future researches as a baseline, which provides generic insights of how to fundamentally enhance pedestrian tracking.     

Semantic fusion of laser and vision in pedestrian detection

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.     

Deep infrared pedestrian classification based on automatic image matting

Infrared pedestrian classification plays an important role in advanced driver assistance systems. However, it encounters great difficulties when the pedestrian images are superimposed on a cluttered background. Many researchers design very deep neural networks to classify pedestrian from cluttered background. However, a very deep neural network associated with a high computational cost. The suppression of cluttered background can boost the performance of deep neural networks without increasing their depth, while it has received little attention in the past. This study presents an automatic image matting approach for infrared pedestrians that suppresses the cluttered background and provides consistent input to deep learning. The domain expertise in pedestrian classification is applied to automatically and softly extract foreground objects from images with cluttered backgrounds. This study generates trimaps, which must be generated manually in conventional approaches, according to the estimated positions of pedestrian’s head and upper body without the need for any user interaction. We implement image matting by adopting the global matting approach and taking the generated trimap as an input. The representation of pedestrian is discovered by a deep learning approach from the resulting alpha mattes in which cluttered background is suppressed, and foreground is enhanced. The experimental results show that the proposed approach improves the infrared pedestrian classification performance of the state-of-the-art deep learning approaches at a negligible computational cost.     

Rapid pedestrian detection in unseen scenes

In this paper, a rapid adaptive pedestrian detection method based on cascade classifier with ternary pattern is proposed. The proposed method achieves its goal by employing the following three new strategies: (1) A method for adjusting the key parameters of the trained cascade classifier dynamically for detecting pedestrians in unseen scenes using only a small amount of labeled data from the new scenes. (2) An efficient optimization method is proposed, based on the cross entropy method and a priori knowledge of the scenes, to solve the classifier parameter optimization problem. (3) In order to further speed up pedestrian detection in unseen scenes, each strong classifier in the cascade employs a ternary detection pattern. In our experiments, two significantly different datasets, AHHF and NICTA, were used as the training set and testing set, respectively. The experimental results showed that the proposed method can quickly adapt a previously trained detector for pedestrian detection in various scenes compared with other existing methods.     

无功补偿性能影响与整定方法

在进行大功率电力电子系统的研制过程中,在没有排除中断延时、接口、计算速度等问题的前提下进行实物实验存在一定的风险.以异步发电机无功补偿技术为例提出HIL和RCP相结合的准实时仿真系统结构,并且给出了HIL和RCP两种准实时仿真时钟处理和参数整定方法.通过进行离线仿真、HIL和RCP准实时仿真,验证了参数整定方法的有效性和两种准实时仿真的可靠性.结果表明,采用该参数整定方法进行的准实时仿真能够有效的模拟实物实验,较为全面地对系统进行测试.     

Associated evolution of a support vector machine-based classifier for pedestrian detection

Support vector machine (SVM) has become a dominant classification technique used in pedestrian detection systems. In such systems, classifiers are used to detect pedestrians in some input frames. The performance of a SVM classifier is mainly influenced by two factors: the selected features and the parameters of the kernel function. These two factors are highly related and therefore, it is desirable that the two factors can be analyzed simultaneously, which are usually not the case in the previous work.In this paper, we propose an evolutionary method to simultaneously optimize the feature set and the parameters for the SVM classifier. Specifically, adaptive genetic operators were designed to be suitable for the feature selection and parameter tuning. The proposed method is used to train a SVM classifier for pedestrian detection. Experiments in real city traffic scenes show that the proposed approach leads to higher detection accuracy and shorter detection time.     

A novel multiplex cascade classifier for pedestrian detection

Reliable pedestrian detection is of great importance in visual surveillance. In this paper, we propose a novel multiplex classifier model, which is composed of two multiplex cascades parts: Haar-like cascade classifier and shapelet cascade classifier. The Haar-like cascade classifier filters out most of irrelevant image background, while the shapelet cascade classifier detects intensively head-shoulder features. The weighted linear regression model is introduced to train its weak classifiers. We also introduce a structure table to label the foreground pixels by means of background differences. The experimental results illustrate that our classifier model provides satisfying detection accuracy. In particular, our detection approach can also perform well for low resolution and relatively complicated backgrounds.     

A framework for pedestrian comfort navigation using multi-modal environmental sensors

Environments significantly influence the sensation of pedestrians, while sensing and navigation technologies can help people improve their trip comfort. In this paper, we present an integrated framework, named NaviComf, which constructs pedestrian navigation systems to improve comfort in time varying environments taking into account the heterogeneous environmental factors. With NaviComf we aim to systematically provide solutions to the four key issues: (1) how to organize the huge amount of sensor data, (2) how to forecast future environmental information, (3) how to incorporate the heterogeneous environmental factors, and (4) how to select optimal paths in time varying environments. We have gathered sensor data of air temperature, relative humidity, and pedestrian congestion in real environments. We have also implemented a prototype system on the basis of the framework using the sensor data. Results of simulations and evaluations show that NaviComf can efficiently provide more comfortable paths as compared with the traditional navigation method.     

电压无功分级控制系统研究

本文主要根据省地协调系统要求及杭州电网目前AVC系统的实际应用情况,对相关系统进行升级改造。强化地调DF8002AVC系统的优化计算功能、开发地调AVC系统与省调／县区AVC系统协调控制功能、将地调AVC系统中原有的操作中心功能和遥控遥调出口转移到市区AVC系统上、开发县区调AVC系统的协调控制功能,从而完成省地县三级AVC协调控制功能。本方案考虑省地县三级协调控制项目中地调AVC系统功能的实现、县区调AVC系统功能的实现、市区AVC系统功能实现。     

Reactive multi-agent system for assembly cell control

This paper presents a multi-agent system for the control of manufacturing systems. The multi-agent system is designed to provide manufacturing control with three important characteristics: high robustness, quick response and good expandability. A prototype multi-agent-based control system has been developed for a flexible assembly cell as an example. The prototype multi-agent system contains several reactive agents. Each agent acts according to its built-in behaviours, and the behaviours are able to respond to stimuli from the manufacturing environment. A system architecture to implement the multi-agent-based control system is proposed and the coordination model of the reactive agents is developed. The generic agent structure is established for individual reactive agents, and the behaviours and the subsumption architecture of each agent are designed.     

Simulation of pedestrian flow through a “T” intersection: A multi-floor field cellular automata approach

There are several ways to study pedestrian dynamics, such as the macroscopic hydrodynamics, molecular dynamics with social force, and cellular automata (CA) with mean field approach. In recent years, the cellular automata approach has received growing interest from researchers. Because this approach is not only to save the computing time but also to reduce the complexity of the problem. In this presentation, we report the study of the conformation of congestion in a “T” intersection by using a cellular automata procedure with multi-floor fields. By using the multi-floor fields, we can mimic the pedestrian flow from several entrances to different exits firstly, and we can simulate the different pedestrian speed by changing the controlling parameter. Our results show, there are some kinds of phase transition in this system.     

User preferences, information transactions and location-based services: A study of urban pedestrian wayfinding

Though research into location-based services (LBS) is being carried out across a number of disciplines, user aspects of LBS remains a cross-cutting theme. In this paper, the research focuses on investigating the user information requirements from LBS at individual level, with emphasis on the interactive nature of information transactions between environments, individuals and mobile devices. Based on a proposed conceptual model, urban pedestrian wayfinding experiments have been implemented in an immersive virtual reality test environment. Automated and semi-automated methods of data collection have allowed an integrated picture of participant behaviour and information preferences to be constructed and analysed. The results of this study show that there are clear user preferences in information requirements in completing wayfinding tasks. However, changes in user preferences during the wayfinding tasks do occur in response to levels of confidence, different spatial layouts and the wayfinding situations individuals encounter. The outcomes indicate that the proposed conceptual interaction model and adopted implementation approach assist in understanding user behaviour and information preferences for LBS.     

行人运动建模技术综述*

随着计算机数值模拟技术的迅速发展,行人运动仿真技术开始成为人群疏散仿真、城市规划和计算机疏散软件开发方面的研究热点。介绍了对行人建模仿真的初步研究,重点介绍了基于行人行为的微观行人运动建模方法,并对各个模型进行了比较分析和研究展望。     

Global robust adaptive path following of underactuated ships

We propose a method for designing a global robust adaptive controller that forces an underactuated ship to follow a reference path under both constant and time-varying disturbances induced by waves, wind and ocean-currents. Both linear and nonlinear damping terms are included to cover both low- and high-speed applications. All nonlinear damping coefficients are assumed unknown but lie in a known compact set. The new results are derived using a choice of an appropriate body-fixed frame origin, a smooth approximation of nonsmooth damping terms, several nonlinear coordinate changes, the backstepping technique, and utilization of the ship dynamic structure. Experiments on a model ship illustrate the results.