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首页> 外文期刊>Industrial Informatics, IEEE Transactions on >CrowdNavi: Demystifying Last Mile Navigation With Crowdsourced Driving Information
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CrowdNavi: Demystifying Last Mile Navigation With Crowdsourced Driving Information

机译：CrowdNavi：使用众包的驾驶信息揭开最后一英里航行的神秘面纱

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摘要

With detailed digital map of the transport network and even real-time traffic, today's navigation services provide good quality routes in the major route level. Once entering the last mile near the destination, they unfortunately can be ineffective and, instead, local drivers often have a better understanding of the routes there. With the deep penetration of 3G/4G mobile networks, drivers today are well connected anytime and anywhere; they can readily access information from the Internet and share information to the driver's community. This motivates our design of CrowdNavi, a complementary service to existing navigation systems, seeking to combat the last mile puzzle. CrowdNavi collects the crowdsourced driving information from users to identify their local driving patterns, and recommend the best local routes for users to reach their destinations. In this paper, we present the architectural design of CrowdNavi and identifies the unique challenges therein, particularly on identifying the last segment in a route from the crowdsourced driving information and navigate drivers through the last segment. We offer a complete set of algorithms to identify the last segment from the drivers' trajectories, scoring the landmark, and locating best routes with user preferences. We then present effective navigation algorithm to locate the best route along the landmarks for the last segment. We further realize the potential risks of attacks in crowdsourced systems and develop a multisensor cross-validation method against them. We have implemented the CrowdNavi app on Android mobile OS, and have examined its performance under various circumstances. The experimental results demonstrate its superiority in navigating drivers in the last segment toward the destination.
机译：借助详细的运输网络数字地图，甚至实时交通信息，当今的导航服务可在主要路线级别上提供高质量的路线。不幸的是，一旦进入目的地附近的最后一英里，它们可能会失效，相反，本地驾驶员通常会对那里的路线有更好的了解。随着3G / 4G移动网络的深入普及，当今的驱动程序可以随时随地连接在一起；他们可以随时从Internet访问信息，并与驾驶员社区共享信息。这激发了我们对CrowdNavi的设计的动力，CrowdNavi是对现有导航系统的补充服务，旨在应对最后一英里的难题。 CrowdNavi从用户那里收集众包的驾驶信息，以识别他们的本地驾驶模式，并为用户推荐到达目的地的最佳本地路线。在本文中，我们介绍了CrowdNavi的体系结构设计，并确定了其中的独特挑战，特别是在从众包驾驶信息中确定路线的最后一段并导航驾驶员通过最后一段的过程中。我们提供了一套完整的算法，可以从驾驶员的轨迹中识别出最后一个路段，对地标进行评分，并根据用户的喜好找到最佳路线。然后，我们提出有效的导航算法，以沿着最后一段的地标定位最佳路线。我们进一步意识到众包系统中遭受攻击的潜在风险，并针对它们开发了一种多传感器交叉验证方法。我们已经在Android移动操作系统上实现了CrowdNavi应用，并检查了其在各种情况下的性能。实验结果证明了其在将最后一段驾驶员导航到目的地方面的优越性。

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