基于云网格集成调度的防拥堵车辆路径规划算法

在道路交通路网中,车辆拥堵问题是流量与路网结构之间相互作用的一个复杂动态过程,通过车辆路径规划,实现对路网网格集成调度,从而提高路网通行吞吐量。传统方法采用并行微观交通动态负载平衡预测算法实现车辆拥堵调度和车辆路径规划,不能准确判断路面上的车辆密度,路径规划效益不好。提出一种基于云网格集成调度的防拥堵车辆路径规划算法,即构建基于Small-World模型的云网格路网模型,采用RFID标签信息进行路况信息采集,实现交通网络拥堵评估信息特征的提取,采用固有模态函数加权平均求得各车道的车辆拥塞状态函数,对所有车道内车辆密度取统计平均可获得簇内的车辆密度。设计交通路网拥堵检测算法来对当前个体道路信息进行一维邻域搜索,从而实现车辆路径规划控制目标函数最佳寻优。通过动态博弈的方式求得车辆防拥堵路径的近似最优轨迹,实现路径规划算法的改进。仿真结果表明,该算法能准确规划车辆路径,实现最优路径控制,从而提高严重拥堵路段的车流速度和路网吞吐性能,性能优越。     

线控转向系统路感控制策略的研究

线控转向系统取消了转向盘与转向轮的机械连接,所以必须通过电机向驾驶员实时反馈路感,从而使驾驶员感知车辆行驶状态和路面状况,作出正确操纵行为.提出了路感的评价指标,并根据转向角度和车速计算轮胎回正力矩,研究了不同力传动比对输入阻抗和转向盘力阶跃响应性能的影响.随车速调整力传动比,可获得驾驶员期望的转向感觉.结果表明,调整力传动比参数可获得良好的路感和回正性.提出的路感控制策略双纽线试验和蛇行实验性能良好,可改善汽车的操纵稳定性.     

Effects of the viewing context on target detection. Implications for road lighting design

The Small Target Visibility (STV) model is the main model used to assess the quality of road lighting installations (IESNA, 2000). However, this model is based on a simple detection task in foveal vision using psychophysical data from laboratory conditions. The purpose of this study was to evaluate the impact of a complex background and apparent motion on target detection performance in mesopic vision, for three luminance contrasts, with reference to the STV scenario. To do so, participants were invited to detect standard square targets varying in terms of contrast presented in three Conditions: a uniform background, still images, and a video. Luminance levels were chosen in the mesopic domain relevant for road lighting at night. Images and video were chosen in relation to a driving task at night. The results showed that both the spatial context and the apparent motion had a negative impact on peripheral target detection performance: contrasts which are easy to detect in conditions close to the STV reference data may lead to poor performance if one adds context variables. These results give evidence that the STV model used for road lighting design based on laboratory data is limited, which strengthens previous results (Mayeur et al., 2008). The results are discussed in relation to the field factor used by practitioners to compensate for the differences between the STV reference scenario (detection of a small square target on a lit road while driving) and the STV psychophysical reference data.     

基于三维激光扫描与无人机倾斜摄影技术的古建筑重建

针对古建筑三维重建中传统技术很难同时兼顾高精度三维信息快速获取和多角度真实地物纹理特征准确采集的难题,提出联合三维激光扫描与倾斜摄影测量技术的古建筑三维重建新方法。新方法充分结合了两种技术的优势，利用特征点匹配算法来实现多源数据精确融合，从而实现古建筑室内外完整三维模型的构建。以中国传统村落渐山村李氏家庙为例，通过该方法构建的三维模型与实际激光测距仪测量数据比较，其相对中误差为5 mm, 且建模效率相对于传统方法也有大幅提升;此外，将构建的模型以不同比例尺进行3D打印与室内外展示，进一步验证了建模数据的可用性;表明该方法将为古建筑文化遗产的修复与保护提供重要技术支持。     

Analyzing Temporal-spatial Change of Urumqi Impervious Surface using Linear Spectral Mixture Analysis

The method of linear spectral mixture analysis combined with V-I-S model(Vegetation-Imperious surface-Soil) is used to estimate the impervious surface abundance of Urumqi city, which is in the core area of the Silk Road Economic Belt, using Landsat OLI and TM multi-spectral data. Because the red color steel shed has low brightness on the impervious surface brightness image, an improvement was proposed, and then verify the accuracy through the interpretation of high-resolution satellite imagery. The results show that: the impervious surface area in Urumqi displayed a significant expansion from 140.41 km² to 462.62 km² during past 24 years (1994 to 2018). It expanded slowly during 1994 to 2005, and increased rapidly since 2005. The expansion intensity increased during 1994~2015 and decreased after 2015; and the spatial expansion of urban impervious surface is significantly different, with the largest expansion area in the west and northeast direction. The comprehensive analyses suggested that the expansion of the impervious surface of Urumqi city is limited by the surrounding mountain topography and coal mining, and the “Urumqi-Changji integration” policy is the major driving factor for urban expansion in the past 24 years.     

基于统计预测的车道边缘点搜索算法

本文提出了车道连续性、直线性假设和边缘点位置的可预测性推论，实现了一种基于统计预测的车道边缘点搜索算法。该算法利用统计预测基本原理对车道边缘点的位置进行预测．将搜索限制在预测范围内，提高了搜索精度，减小了整幅图像的搜索范围，保证了算法的实时性能。     

Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles

Nowadays, the path routing over road networks has become increasingly important, yet challenging, in many real-world applications such as location-based services (LBS), logistics and supply chain management, transportation systems, map utilities, and so on. While many prior works aimed to find a path between a source and a destination with the smallest traveling distance/time, they do not take into account the quality constraints (e.g., obstacles) of the returned paths, such as uneven roads, roads under construction, and weather conditions on roads. Inspired by this, in this paper, we consider two types of ad-hoc obstacles, keyword-based and weather-based obstacles, on road networks, which can be used for modeling roads that the returned paths should not pass through. In the presence of such ad-hoc obstacles on roads, we formulate a path routing query over road networks with ad-hoc obstacles (PRAO), which retrieves paths from source to destination on road networks that do not pass ad-hoc keyword and weather obstacles and have the smallest traveling time. In order to efficiently answer PRAO queries, we design effective pruning methods and indexing mechanism to facilitate efficient PRAO query answering. Extensive experiments have demonstrated the efficiency and effectiveness of our approaches over real/synthetic data sets.     

基于天空分割的去雾算法在目标检测中的应用

针对道路目标检测的准确率受雾霾环境影响的问题,提出一种基于雾检测与天空分割的自适应去雾算法。提取道路环境中的雾检测区域并对其进行通道相关性以及亮度和饱和度比值的计算,以判别场景类别;对图像进行天空非天空区域的分割并对不同区域建立去雾模型,用于自适应去雾;将恢复后的图像应用到道路目标检测算法中,进行去雾效果验证和道路目标检测。实验结果表明,该算法提高了雾霾环境下目标检测的准确率。     

路网中双色数据集上连续反向k近邻查询处理的研究

近年来,反向最近邻查询(RNN)算法研究得到了普遍的关注,成为了数据库领域的一个研究热点。欧氏空 间中提出了较多的高效算法,而路网中的反向最近邻处理方面所做的工作不够,有关这方面的成果较少。路网中查询 点和数据对象之间以及不同数据对象之间的距离受到路网连通性的影响,欧氏空间中的反向最近部方法在路网中不 适用。反向最近部查询有两种类型:单色反向最近部查询(Monochromatic RNN, MRNN)和双色反向最近部查询(13i- chromatic RNN,13RNN)。到目前为止,仍然没有有效的算法来处理路网中双色数据集上的连续反向k近部查询。因 此,研究路网中双色数据集上连续反向k近部查询是很有意义的。     

GML 3.0应用模式的开发及应用研究

简单介绍了GML3．0,然后详细阐述了GML3．0的主要模式;论述了GML应用模式开发的若干技术问题;最后提出了基于GML的城市道路网络模型,并实现了基于GML的空间数据转换引擎。