Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method

We tried more precise mapping of vegetation using UAV?(unmanned aerial vehicle), as a new method of creating vegetation maps, and we?objected to be clearly the efficient mapping of vegetation using the UAV method by comparing vegetation maps created by analysing aerial photographs taken by a UAV and an aircraft (manned flight). The aerial photography using UAV was conducted in the Niida River estuary (the secondary river flowing into Minamisoma City in Fukushima Prefecture, Japan). The photography period was in August 2013. We analysed the aerial photographs using ArcGis 9 (Esri Japan Corporation, Tokyo, Japan). The aerial photographs of the main plant communities (Phragmites australis,?Typha domingensis, and?Miscanthus sacchariflorus) taken by the UAV could clearly discriminate each plant community at the 1/50 scale. Moreover, it could clearly discriminate the shape of a plant at the 1/10 scale. We compared the vegetation maps by analysing the aerial photos taken by a UAV (2013 shooting) and an aircraft (2011 shooting). As a result, the vegetation map created by the UAV method could clearly discriminate community distributions. We conclude that vegetation surveys using UAV are possible and are capable of a highly precise community division in places where field reconnaissance is difficult. The UAV method is effective and will contribute to the improvement of research methods in the future;this method may reduce research costs associated with a reduction in field survey days and man-power.

ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Autonomous Vehicle Design in Lean Product Development Processes for Value Stream Map

The global automotive industry is giving a difficult and common test in order to create advanced life models in the near future plans or scenarios that include current autonomous vehicle technologies. Therefore, the main purpose of the research is to comparatively evaluate the impact of autonomous vehicle technologies, which are newly included in the automotive manufacturing industry under sustainable competition, on lean product development processes, value acquisition and preservation, in different organizational structures in the approach. Although mergers or brand acquisitions in the global automotive industry create joint R & D (Research Development) or joint new P & D (Product Development) process structures for the development of autonomous vehicle technologies, heavy competition continues in the market. These new processes create different needs for the merger and partnership of the renewed traffic infrastructures under national and international regulations, and for the implementation of the new autonomous life model. Firm and brand marriages, mergers or acquisitions in today’s automotive industry have ensured the high diffusion of lean product development processes under the stream of value creation or preservation carried out specific to the company under competition. Brand mergers in automotive industry companies struggling to survive under high competition create new work disciplines, professions, and engineering flow steps in lean product development processes. However, lean product development processes driven by technological innovation under simplification have resulted in the integration of parts and systems within the autonomous vehicle design structure, as well as creating new interdisciplinary value streams or different stakeholders. Therefore, the research revealed the significant effects of lean product development processes on the value stream in the automotive industry, on the mixed and lean product development process structure formed by new or existing vehicle systems (conventional vehicle) under the penetration of each existing and new discipline. This research compares the efficient operation steps of the process stakeholders in the autonomous vehicle design parts or systems containing innovation and new technology together with the value stream in the lean product development process, and the new process stakeholder’s business-oriented global and local automotive industry companies. New autonomous vehicle technologies, together with their unique software, hardware and development analysis, have been involved in the lean product development process with their interdisciplinary studies or expertise. Therefore, the study firstly focused on the technologies in environmental use together with the new basic features of autonomous vehicles, and then examined in depth the new or existing disciplines and interdisciplinary basic structure that these innovations affect under the value stream in the lean product development process. In addition, micro-level results and recommendations were shared, shedding light on how autonomous vehicle levels will create changes in the new product development process.

基于快速效率MAP图的外转子永磁同步电机优化设计

电动汽车复杂的运行工况要求驱动电机转速-转矩范围和高效率区范围宽,而电机效率MAP图是评估驱动电机是否满足上述要求最直观的工具。但是,电机效率MAP图通常需要台架测试来获取,费时费力,无法满足电动汽车敏捷开发过程中进行快速验证和优化迭代的要求。为解决这一问题,文章对轮毂电机中使用的外转子永磁同步电机,通过等效磁路法计算电机电磁参数值,再结合电机控制方法对电机全转速-转矩区间损耗进行计算,最后在MATLAB中编程实现电机效率MAP图的快速计算。基于此方法,结合遗传算法,可以实现电机结构参数的快速优化设计,进而提高产品开发效率。

Analysis of unmanned aerial vehicle navigation and height control system based on GPS

According to the characteristic of global positioning system（GPS） reflection signals,a GPS delay mapping receiver system scheme is put forward,which not only satisfies the unmanned aerial vehicle（UAV） guidance localization but also realizes height measurement.A code delay algorithm is put forward,which processes the direct and land reflected signal and outputs the navigation data and specular point.The GPS terrain reflected echo signal mathematical equation is inferred.The reflecting signal area,when the GPS signal passes the land,is analyzed.The height survey model reflected land surface characteristic is established.A simulation system which carries guidance localization of the UAV and the height measuring control through the GPS direct signal and the land reflected signal is designed,taken the GPS satellite as the illumination source,the receiver is put on the UAV.Then the UAV guidance signal,the GPS reflection signal and receiver＇s parallel processing are realized.The parallel processing reduces UAV＇s payload and raises system＇s operating efficiency.The simulation results confirms the validity of the model and also provides the basis for the UAV＇s optimization design.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

Generation and Analysis of Hybrid-Electric Vehicle Transmission Shift Schedules with a Torque Split Algorithm

The increased concern over global climate change and lack of long-term sustainability of fossil fuels in the projected future has prompted further research into advanced alternative fuel vehicles to reduce vehicle emissions and fuel consumption. One of the primary advanced vehicle research areas involves electrification and hybridization of vehicles. As hybrid-electric vehicle technology has advanced, so has the need for more innovative control schemes for hybrid vehicles, including the development and optimization of hybrid powertrain transmission shift schedules. The hybrid shift schedule works in tandem with a cost function-based torque split algorithm that dynamically determines the optimal torque command for the electric motor and engine. The focus of this work is to develop and analyze the benefits and limitations of two different shift schedules for a position-3 (P3) parallel hybrid-electric vehicle. a traditional two-parameter shift schedule that operates as a function of vehicle accelerator position and vehicle speed (state of charge (SOC) independent shift schedule), and a three-parameter shift schedule that also adapts to fluctuations in the state of charge of the high voltage batteries (SOC dependent shift schedule). The shift schedules were generated using an exhaustive search coupled with a fitness function to evaluate all possible vehicle operating points. The generated shift schedules were then tested in the software-in-the-loop (SIL) environment and the vehicle-in-the-loop (VIL) environment and compared to each other, as well as to the stock 8L45 8-speed transmission shift schedule. The results show that both generated shift schedules improved upon the stock transmission shift schedule used in the hybrid powertrain comparing component efficiency, vehicle efficiency, engine fuel economy, and vehicle fuel economy.

Highway Toll Collection Method for Connected Automated Vehicle Platooning Using Spatio-Temporal Grid Reservation

In the intelligent transportation system, the autonomous vehicle platoon is a promising concept for addressing traffic congestion problems. However, under certain conditions, the platoon’s advantage cannot be properly developed, especially when stopping for electronic toll collection (ETC) to pay the toll fee using the highway. This study proposes a software architectural platform that enables connected automated vehicles to reserve a grid-based alternative approach to replace current highway toll collection systems. A planned travel route is reserved in advance by a connected automated vehicle in a platoon, and travel is based on reservation information. We use driving information acquired by communication mechanisms installed in connected automated vehicles to develop a dynamic map platform that collects highway toll tax based on reserving spatio-temporal grids. Spatio-temporal sections are developed by dividing space and time into equal grids and assigning a certain road tax rate. The results of the performance evaluation reveal that the proposed method appropriately reserves the specified grids and collects toll taxes accurately based on a spatio-temporal grid with minimal communication time and no data package loss. Likely, using the proposed method to mediate driving on a one-kilometer route takes an average of 36.5 seconds, as compared to ETC and the combination of ETC and freeway road lane methods, which take 46.6 and 53.8 seconds, respectively, for 1000 vehicles. Consequently, our proposed method’s travel time improvements will reduce congestion by more effectively exploiting road capacity as well as enhance the number of platoons while providing non-stoppable travel for autonomous vehicles.

GPS/GIS based vehicle dispatching for opencast mines

An integrated GPS and GIS based vehicle dispatching system was presented. The system uses GIS technology for the development of digital mine map database and GPS for vehicle positioning. The system consists of five modules: position module incorporated GPS and dead reckoning (DR); a map database structure for displaying and guidance purposes; a routing module based on the map database is able to give out the best route for the vehicles; map matching and route guidance module put the vehicle position to its exact location on the road despite of errors in positioning and map data; and the client-server module allows client exchange information between driver and control centre. The system can be operated in client-server level in which users can request routing and guidance with devices such as hand phone and PDA by communicating their current positions to the server or runs in autonomous mode when users cannot reach the server.

A Design Method of Noncoherent Unitary Space-Time Codes

We generalized an constructing method of noncoherent unitary space time codes (N-USTC) over Rayleigh flat fading channels. A family of N-USTCs with T symbol peroids, M transmit and N receive antennas was constructed by the exponential mapping method based on the tangent subspace of the Grassmann manifold. This exponential mapping method can transform the coherent space time codes (C-STC) into the N-USTC on the Grassmann manifold. We infered an universal framework of constructing a C-STC that is designed by using the algebraic number theory and has full rate and full diversity (FRFD) for t symbol periods and same antennas, where M, N, T, t are general positive integer. We discussed the constraint condition that the exponential mapping has only one solution, from which we presented a approach of searching the optimum adjustive factor αopt that can generate an optimum noncoherent codeword. For different code parameters M, N, T, t and the optimum adjustive factor αopt, we gave the simulation results of the several N-USTCs.

Improved lazy theta algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By using the data structure of octree,the octree map is constructed,and the search nodes is significantly reduced.Then,the lazy theta*algorithm,including neighbor node search,line-of-sight algorithm and heuristics weight adjustment is improved.In the process of node search,UAV constraint conditions are considered to ensure the planned path is actually flyable.The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes.Heuristic weight adjustment strategy is employed to control the precision and speed of search.Finally,the simulation results show that the improved lazy theta*algorithm is suitable for path planning of UAV in complex environment with multi-constraints.The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

A Data-Driven Intersection Geometry Mapping Technique to Enhance the Scalability of Trajectory-Based Traffic Signal Performance Measures

Connected vehicle (CV) trajectory data provides practitioners with opportunities to assess traffic signal performance with no investment in detection or communication infrastructure. With over 500 billion trajectory records generated each month in the United States, operations can be evaluated virtually at any of the over 400,000 traffic signals in the nation. The manual intersection mapping required to generate accurate movement-level trajectory-based performance estimations is the most time-consuming aspect of using CV data to evaluate traffic signal operations. Various studies have utilized vehicle location data to update and create maps;however, most proposed mapping techniques focus on the identification of roadway characteristics that facilitate vehicle navigation and not on the scaling of traffic signal performance measures. This paper presents a technique that uses commercial CV trajectory and open-source OpenStreetMap (OSM) data to automatically map intersection centers and approach areas of interest to estimate signal performance. OSM traffic signal tags are processed to obtain intersection centers. CV data is then used to extract intersection geometry characteristics surrounding the intersection. To demonstrate the proposed technique, intersection geometry is mapped at 500 locations from which trajectory-based traffic signal performance measures are estimated. The results are compared to those obtained from manual geometry definitions. Statistical tests found that at a 99% confidence level, upstream-focused performance estimations are strongly correlated between both methodologies. The presented technique will aid agencies in scaling traffic signal assessment as it significantly reduces the amount of manual labor required.

Gravitational Space-Time Curve Generation via Accelerated Charged Particles

A force with an acceleration that is equal to multiples greater than the speed of light per unit time is exerted on a cloud of charged particles. The particles are resultantly accelerated to within an infinitesimal fraction of the speed of light. As the force or acceleration increases, the particles’ velocity asymptotically approaches but never achieves the speed of light obeying relativity. The asymptotic increase in the particles’ velocity toward the speed of light as acceleration increasingly surpasses the speed of light per unit time does not compensate for the momentum value produced on the particles at sub-light velocities. Hence, the particles’ inertial mass value must increase as acceleration increases. This increase in the particles’ inertial mass as the particles are accelerated produce a gravitational field which is believed to occur in the oscillation of quarks achieving velocities close to the speed of light. The increased inertial mass of the density of accelerated charged particles becomes the source mass (or Big “M”) in Newton’s equation for gravitational force. This implies that a space-time curve is generated by the accelerated particles. Thus, it is shown that the acceleration number (or multiple of the speed of light greater than 1 per unit of time) and the number of charged particles in the cloud density are surjectively mapped to points on a differential manifold or space-time curved surface. Two aspects of Einstein’s field equations are used to describe the correspondence between the gravitational field produced by the accelerated particles and the resultant space-time curve. The two aspects are the Schwarzchild metric and the stress energy tensor. Lastly, the possibility of producing a sufficient acceleration or electromagnetic force on the charged particles to produce a gravitational field is shown through the Lorentz force equation. Moreover, it is shown that a sufficient voltage can be generated to produce an acceleration/force on the particles that is multiples greater than the speed of light per unit time thereby generating gravity.

基于分层2.5D地图的无人履带车辆路径规划

针对越野环境地形下无人履带车辆难以安全、高效规划出一条可通行路径的问题,提出一种考虑地形特征、障碍物以及车辆运动特性的分层式路径规划算法.算法先通过三维参考路径规划模块改进A*算法节点扩展方式,使用无梯度迭代平滑算法、B样条曲线拟合得到平滑参考路径;然后在多阶段状态空间采样模块中采用自适应采样策略生成候选路径集,采用贪婪策略选出最优路径.试验结果表明,所提方法在越野环境中的规划结果既保证了可行性、稳定性、实时性,又在稳定车辆姿态上具有优势.

面向港口环境精细感知的无人船多传感器融合SLAM系统

针对港口环境高精度感知需求,综合考虑影响同时定位与建图(SLAM)精度的港口环境因素,提出基于多传感器融合的激光SLAM环境感知方案。通过分析多种传感器对港口SLAM环境感知的影响,引入惯性测量传感器弥补激光SLAM输出频率低和剧烈运动位姿估计不准确等缺陷,采用卫星定位系统信息进行高程数据约束,处理船舶运动特性导致的垂荡累计漂移。从应用需求出发,对传感器进行选型和布置优化,搭建基于无人船的港口环境多传感器融合SLAM系统。结果表明,提出的港口环境高精度点云地图获取方案能在典型港口场景下准确实时建图,为水面精细SLAM提供技术支持。

基于AirSim的无人机硬件在环仿真平台设计与实现

为了减少使用真实无人机和相机进行算法验证所带来的高成本与人员安全风险,研制了一个无人机实物平台,并基于AirSim和虚幻引擎4搭建了配套的无人机硬件在环仿真平台。在同一局域网内,仿真平台与实物平台建立连接,将传感器数据直接输送到实物平台进行算法验证。硬件在环仿真平台可在室内无GPS环境下保证算法在仿真和实物运行时处于同一平台,节省仿真时编译代码和数据拷贝的时间,有利于多传感器融合定位算法的验证。

融合视觉与运动学的车辆综合定位研究

针对在城市峡谷、隧道等卫星定位系统失效的情况下,采用单目相机、消费级惯性测量单元和车辆底盘数据作为定位系统输入,为智能车提供定位信息。首先提出动态的轮胎周长模型,并通过对相关参数建立非线性最小二乘问题,求解出待标定的参数优化值,对基于运动学的车辆里程计进行了改进。将改进后里程计的数据融入到定位系统前端的尺度因子初始化过程中,减少车辆启动阶段惯性测量单元噪声产生的不利影响,提高了系统初始化的稳定性。在后端部分增加了车辆的运动学约束,同时进行非线性优化。通过实车实验将其与VINS-Fusion算法结果对比,证明在初始化稳定性与车辆定位精度两方面上的提高。

激光雷达/IMU/车辆运动学约束紧耦合SLAM算法

针对自动驾驶车辆在全球导航卫星系统(GNSS)信号不足场景下的定位需求,提出了一种激光雷达/惯性测量装置(IMU)/车辆运动学约束紧耦合的同时定位与地图构建(SLAM)算法。首先,基于IMU角速度、车辆后轴轮速和前轮转角构建车辆运动学约束,将车辆运动的位移和姿态信息解耦,构建位移和姿态约束以提高优化结果的准确性;然后,根据点云特征点数量和车辆转向角度引入自适应调整系数,实时调节车辆运动学约束的权重。最后,基于IMU角速度和车辆后轴轮速构建里程计模型,为后端紧耦合优化提供精准的初始值,避免陷入局部最优。不同道路场景下的测试结果表明,所提算法与LeGO_LOAM和LIO_SAM算法相比,平均平面定位精度分别提高了32%和29%,为自动驾驶车辆提供了一种GNSS信号不足情况下的短时高精度定位解决方案。