Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments.Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research.     

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通过视频监控项目的实施加强了企业在开放式园区内车间、办公区域、要害部门、周边围墙、仓储、物流等重要区域的安防管理,使得各级管理人员能够及时了解各主要管理环节的实时安全情况,了解生产车间是否按5S要求整理,送线员工是否按时送线,车间是否有闲杂人员出入、监控厂区是否有不安全因素,以便处理各种突发事件。同时,通过管理规范与系统实现的有效结合实现了"历史"的再现,增强了企业管理检查的可追溯性。     

用超声波法检测钢管混凝土质量的研究

对超声波检测钢管混凝土质量的原理及方法进行研究;结合钢管混凝土拱桥的大比例模型试验,探讨了进一步提高用超声法检测和评价钢管混凝土质量准确性的途径和方法,提出了首波声时法检测和评价钢管混凝土的判别方法;将波形识别法和首波频率法与首波声时法相结合,综合判别以提高判别缺陷的类型和严重程度的准确性。研究结果表明,用所提出的方法较单一用首波声时法判别钢管混凝土质量更具有实用性。     

便携式450MHz无线数据传输综合检测设备研制方案

通过对450MHz无线调度命令维护检修工作的研究,提出了一种便于现场检修维护人员使用的综合维护检测设备,有利于快速定位故障范围、排查设备故障,从而可以解决实际工作中检修手段缺乏、检修设备繁杂、检修过程繁琐、检测工作效率低下的问题。     

船舶电网参数基于DSP的液晶显示的研究

根据点阵液晶显示模块SED1335在船舶电网参数检测中的应用,提出了一种控制液晶显示的实现方法。针对液晶显示控制系统中遇到的大量数据的实时高速传输问题,采用具有快速的计算能力和强大的信息处理能力的数字信号处理器。具体介绍了数字处理芯片TMS320F2812及液晶模块SED1335的功能特性和控制参数,并提出以TMS320F2812为控制核心的硬件电路,给出了C语言实现软件设计的具体方法。     

电脑仿真技术引入船舶主机性能测试的研究

聚苯胺（PANI）纳米纤维同时具有PANI优异的物理、化学性能以及纳米纤维特有的小尺寸效应,在生产和生活领域中具有潜在的应用前景。PANI纳米纤维制备方法主要包括物理法、化学法、电化学法和综合法等。综述了不同方法的制备原理、优势及存在的问题,重点介绍了化学法中的种子聚合法、界面聚合法和乳液聚合法,并预言综合使用各种聚合法将成为制备PANI纳米纤维的趋势。     

高速公路车辆超速检测算法影响因素分析与对策研究

针对高速公路上车辆违章超速行为，给出了一种基于虚拟视频检测线的车辆速度检测与违章超速行为判定的实用算法。对影响该算法精度的各种影响因素进行了详细的分析，特别是针对检测线的数量与间距设置、图像采集卡的采集频率等对算法的影响进行了细致的讨论，并给出了算法条件下，所能检测的最高理论车速。对在应用中可能存在的漏检、误检情况，提出了解决办法。通过实验验证了该算法具有良好的适应性和较高的检测率。     

Robust real-time pedestrians detection in urban environments with low-resolution cameras

Detecting that pedestrians are present in front of a vehicle is highly desirable to avoid dangerous traffic situations. A novel vision-based system is presented to automatically detect far-away pedestrians with low-resolution cameras mounted in vehicles given the contributions of fixed cameras present in the scene.Fixed cameras detect pedestrians by solving an inverse problem built upon a multi-class dictionary of atoms approximating the foreground silhouettes. A sparse-sensing strategy is proposed to extract the foreground silhouettes and classify them in real-time. Mobile cameras detect pedestrians given only their appearance in the fixed cameras. A cascade of compact binary strings is presented to model the appearance of pedestrians and match them across cameras.The proposed system addresses the practical requirements of transportation systems: it runs in real-time with low memory loads and bandwidth consumption. We evaluate the performance of our system when extracted features are severely degraded and the sensing devices are of low quality. Experimental results demonstrate the feasibility of our collaborative vision-based system.     

Strategic de-confliction in the presence of a large number of 4D trajectories using a causal modeling approach

This paper presents a strategic de-confliction algorithm based on causal modeling developed under the STREAM project and launched under the umbrella of the Single European Sky ATM Research (SESAR) Program. The basic underlying concept makes use of the enriched information included in the Shared Business Trajectories (SBTs) of the flights prior to takeoff (or in the Reference Business Trajectories (RBTs) if the flight is airborne) to allocate conflict-free trajectories in a traffic planning phase that should lead to an actual conflict-free scenario in the flight execution phase in the absence of flight and/or network uncertainties. The proposed approach could decrease the workload of the air traffic controllers, thus improving the Air Traffic Management (ATM) capacity while meeting the maximum possible expectations of the Airspace Users’ requirements in terms of horizontal flight efficiency. The main modules of the implemented system are also presented in this paper; these modules are designed to enable the processing of thousands of trajectories within a few seconds or minutes and encompass a global network scope with a planning horizon of approximately 2–3 h. The causal model applied for network conflict resolution and flight routing allocation is analyzed to demonstrate how the emergent dynamics (i.e., domino effects) of local trajectory amendments can be efficiently explored to identify conflict-free Pareto-efficient network scenarios. Various performance indicators can be taken into account in the multi-criteria optimization process, thus offering to the network manager a flexible tool for fostering a collaborative planning process.     

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.