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 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.     

Reactive pedestrian path following from examples

Architectural and urban planning applications require animations of people to present an accurate and compelling view of a new environment. Ideally, these animations would be easy for a non-programmer to construct, just as buildings and streets can be modeled by an architect or artist using commercial modeling software. In this paper, we explore an approach for generating reactive path following based on the users examples of the desired behavior. The examples are used to build a model of the desired reactive behavior. The model is combined with reactive control methods to produce natural 2D pedestrian trajectories. The system then automatically generates 3D pedestrian locomotion using a motion-graph approach. We discuss the accuracy of the learned model of pedestrian motion and show that simple direction primitives can be recorded and used to build natural, reactive, path-following behaviors.     

行人惯性导航零速检测算法

在行人惯性导航中,零速检测是实现速度误差清零和导航误差估计的前提,有着重要的作用.针对行人运动过程中零速区间时间间隔短难以检测的问题,提出了一种基于人体脚部运动特征的零速检测算法,将步行运动抽象成了一个包含4个隐含状态与15个观测量的隐马尔可夫模型,并阐述了模型构建机理.利用Baum-Welch算法训练和优化模型参数,提高了检测准确率.实验结果表明:所提出的方法零速检测效果较好,且采用该方法的行人惯性导航系统,其定位误差约为行进距离的0.73％,定位精度较高.     

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.     

Pedestrian navigation using the sense of touch

Haptics is a feedback technology that takes advantage of the human sense of touch by applying forces, vibrations, and/or motions to a haptic-enabled user device such as a mobile phone. Historically, human–computer interaction has been visual, data, or images on a screen. Haptic feedback can be an important modality in Mobile Location-Based Services like – knowledge discovery, pedestrian navigation and notification systems. In this paper we describe a methodology for the implementation of haptics in four distinct prototypes for pedestrian navigation. Prototypes are classified based on the user’s navigation guidance requirements, the user type (based on spatial skills), and overall system complexity. Here haptics is used to convey location, orientation, and distance information to users using pedestrian navigation applications. Initial user trials have elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. We also tested the spatial ability of the user to navigate using haptics and landmark images based navigation. This was followed by a test of memory recall about the area. Users were able to successfully navigate from a given origin to a Destination Point without the use of a visual interface like a map. Results show the users of haptic feedback for navigation prepared better maps (better memory recall) of the region as compared to the users of landmark images based navigation.     

Embedded visual behaviors for navigation

The implementation of a set of visually based behaviors for navigation is presented. The approach, which has been inspired by insect's behaviors, is aimed at building a “library” of embedded visually guided behaviors coping with the most common situations encountered during navigation in an indoor environment. Following this approach, the main goal is no longer how to characterize the environment, but how to embed in each behavior the perceptual processes necessary to understand the aspects of the environment required to generate a purposeful motor output.

The approach relies on the purposive definition of the task to be solved by each of the behaviors and it is based on the possibility of computing visual information during the action. All the implemented behaviors share the same input process (partial information of the image flow field) and the same control variables (heading direction and velocity) to demonstrate both the generality of the approach as well as its efficient use of the computational resources. The controlled mobile base is supposed to move on a flat surface but virtually no calibration is required of the intrinsic and extrinsic parameters of the two cameras and no attempt is made at building a 2D or 3D map of the environment: the only output of the perceptual processes is a motor command.

The first behavior, the centering reflex allows a robot to be easily controlled to navigate along corridors or following walls of a given scene structure. The second behavior extends the system capabilities to the detection of obstacles lying on the pavement in front of the mobile robot. Finally docking behaviors to control the robot to a given position in the environment, with controlled speed and orientation, are presented.

Besides the long-term goal of building a completely autonomous system, these behaviors can have very short-term applications in the area of semi-autonomous systems by taking care of the continuous, tedious control required during routine navigation.     

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。     

行人导航系统中航迹推算参数估计方法的研究

将GPS/DR组合导航技术应用到行人导航系统中。利用加速度计对行人步态进行判别,将神经网络用于行人步幅信息的标定。同时利用电子罗盘实现方位角的测量,并结合加速度计信息进行倾斜角度的误差补偿。现场测试结果表明,本文提出的DR参数估计方法不但提高参数估计的精度,而且能够满足行人导航定位的要求。     

行人运动建模及仿真研究综述

根据行人运动建模及仿真发展历史以及已经提出的一些模型和算法,从宏观和微观两个层面对行人运动建模策略进行系统分析,包括宏观建模策略:行人动力学模型、空间交互/熵最大化模型以及微观建模策略:元胞自动机模型、磁力场模型、社会力模型、智能体模型、空间句法模型、马尔可夫模型.在建模策略分析基础上,重点研究了Legion,PAXPORT,NOMAD,STEPS以及BuildingEXODUS五种主流行人仿真系统,总结了各系统的建模思想、核心模型及优缺点.指出了未来行人系统建模及仿真的研究趋势.     

ACCLMesh: curvature‐based navigation mesh generation

The proposed method computes a navigation mesh for arbitrary and dynamic 3D environments based on curvature and is robust and efficient. This method addresses a number of known limitations in state‐of‐the‐art techniques to produce navigation meshes that are tightly coupled to the original geometry, incorporate geometric details that are crucial for movement decisions, can robustly handle complex surfaces and can efficiently repair the navigation mesh to accommodate dynamically changing environments. The method is integrated into a standard navigation and collision avoidance system to simulate thousands of agents on complex 3D surfaces in real time. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

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.     

Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice

Large environments that are designed for travel, leisure, and for everyday life – such as transport hubs, amusement parks, and shopping centers – feature different locations that are frequently visited by pedestrians. Each visit is evoked by one’s motivation to engage in some kind of activity at a certain location. By means of modeling the pedestrians’ interests in locations with the aid of computer simulations, it is possible to forecast the occupancy at locations by utilizing sophisticated pedestrian destination choice models. In the field of pedestrian dynamics research, location preference modeling is not common, but it is all the more rare to include a psychological grounding into such choice models. Here we show that our psychologically inspired and mathematically defined model to describe pedestrians’ interests in locations is able to improve the exactness of pedestrian destination choice models. The interest function model is based on the psychological concept of goal-related memory accessibility and on fundamental coherences found in pedestrian-related data that is measurable at locations. We validated the interest function model and our results provide evidence that our approach improves the simulation fidelity regarding occupancy forecasting. Because the interest concept is designed as a framework that can be coupled to existing microscopic pedestrian simulators, it can be used in most pedestrian destination choice models to describe pedestrian visiting preferences. Consequently, the reliability of the occupancy predictions of pedestrian simulations can be enhanced by integrating the interest function model into choices models.     

一种INS-GPS-DVL容错组合导航系统

针对某船载导航系统精度和容错性能不能满足应用要求的问题,提出了一种基于信息融合技术的容错组合导航系统方案。研究了该组合导航系统的滤波算法,通过半物理仿真实验研究了该导航系统的性能。结果表明：所设计的组合导航系统可提供高精度的导航信息,提高导航系统综合性能。     

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.     

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.     

组合导航计算系统的Simulink仿真

采用联邦卡尔曼滤波算法,分析了INS/GPS/ADS/CNS组合导航计算系统的数学模型,然后以MATLAB/Simulink为平台,构建了INS/GPS/ADS/CNS组合导航计算系统的仿真模型并进行了仿真。结果表明:建立的仿真模型能正确仿真组合导航计算系统的工作过程,并且具有良好的可视化效果,为组合导航系统的研究提供了有效的工具。     

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.     

内河枯水期航行仿真建模及实现技术研究

内河航运是综合运输体系中的重要组成部分,然而由于受季节性等影响,内河航道变化较大,容易导致碍航现象发生,影响内河航运安全畅通和运行效率。针对枯水期内河航运特点,研究了内河航道建模及航行仿真方法。提出了一套内河航道建模、水面生成和船舶随机产生方法,为研究枯水期提高内河航道通行效率、保障安全畅通提供了一个有效的平台。