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    • 1. 发明申请
    • METHOD AND SYSTEM FOR CROWD SENSING TO BE USED FOR AUTOMATIC SEMANTIC IDENTIFICATION
    • 用于自动语义识别的波纹感测的方法和系统
    • US20150285639A1
    • 2015-10-08
    • US14636153
    • 2015-03-02
    • Umm-Al-Qura University
    • Anas BasalamahHEBA ALYMoustafa Amin Youssef
    • G01C21/26H04W4/02
    • H04W4/027G01C21/32H04W4/023
    • The Map++ as a system and method that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others is described. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our results show that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.
    • 描述了将Map ++作为一种系统和方法,利用众所周知的标准手机传感器,自动丰富数字地图,使用不同的道路语义,如隧道,颠簸,桥梁,行人天桥,人行横道,道路容量等。 我们的分析表明,车辆或行走中的人类手机传感器受到不同道路特征的影响,可以开采以扩展免费和商业地图服务的功能。 我们介绍Map ++的设计和实现,并在一个大城市进行评估。 我们的研究结果表明,我们可以准确检测不同的语义,具有3%以上的假阳性率和6%的假阴性率。 此外,我们还表示,Map ++在手机上占用的能量很小,突显出其作为无处不在的数字地图丰富服务的承诺。
    • 4. 发明申请
    • METHOD AND SYSTEM FOR AN ACCURATE AND ENERGY EFFICIENT VEHICLE LANE DETECTION
    • 用于精确和有效的车辆检测的方法和系统
    • US20160091609A1
    • 2016-03-31
    • US14661272
    • 2015-03-18
    • Umm-Al-Qura University
    • Heba Allah Aly AbdEl-Halim Aly IsmailAnas BasalamahMoustafa Amin Youssef
    • G01S19/13
    • G01S19/49
    • Knowledge of the vehicle's lane position is required for several location-based services such as advanced driver assistance systems, driverless cars, and predicting driver's intent, among many other emerging applications. We present LaneQuest: a system and method that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the vehicle's current lane. LaneQuest leverages the phone sensors about the surrounding environment to detect the vehicle's lane. For example, a vehicle making a right turn most probably will be in the right-most lane, a vehicle passing by a pothole will be in a specific lane and the vehicle angular velocity when driving through a curve reflects its lane. The ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane span distribution of the different lane-level anchors.
    • 在许多其他新兴应用中,对于诸如基于位置的服务,诸如先进的驾驶员辅助系统,无人驾驶汽车以及预测驾驶员的意图是必需的。 我们提供LaneQuest:利用商用智能手机中普遍存在的低能量惯性传感器来提供车辆当前车道的准确估计的系统和方法。 LaneQuest利用围绕周围环境的手机传感器来检测车辆的车道。 例如,右转的车辆最有可能在最右侧的车道中,通过坑洞的车辆将处于特定的车道,并且当通过曲线行驶时的车辆角速度反映其车道。 模糊位置,传感器噪声和模糊车道锚点; LaneQuest采用新颖的概率车道估计算法。 此外,它使用无监督的人群来源方法来了解不同车道级锚点的位置和车道跨度分布。