Intelligent mobility management in emerging vehicular networks

车辆网络是一种新兴的网络，可以将车辆相互连接并将其与道路基础设施连接起来。在这种网络中管理移动性提出了我们希望在本提案中解决的几个挑战。这些网络可以实现不同类型的应用程序：用于避免碰撞和道路作业的安全应用，例如，用于自动驾驶辅助的实时应用、用于管理交通和建议绕行的智能运输系统（ITS）应用、诸如高速公路上的自动收费支付、与在线多媒体内容的连接等舒适应用。为了有效地运行，车辆对计算能力以及几乎连续的连接的需求增加。我们在此提出的研究计划侧重于新兴车辆网络中的智能移动管理。它涵盖了移动的网络、人工智能、机器学习、数据科学和数学优化等广泛领域，以解决车辆造成的道路拥堵和交通堵塞问题。 本研究计划的主要目标是设计智能方法，算法和工具，通过交通拥堵预测，检测和控制，有效地管理车辆的移动性在真实的时间。从长远来看，这项研究计划的目的是提出一个移动性管理架构的下一代自组织车辆网络的基础上，机器学习和赋予的能力，以预测交通拥堵或减少it.To实现这一目标，我们提出了一种方法的基础上的四个V的特点大数据：体积，品种，准确性和可变性。我们的长期架构将允许从网络生成的数据和用户中收集、提取和理解相关信息，以推断战略行动和决策，从而检测、控制和预测交通拥堵，并为道路使用者提供更好的服务质量和更好的体验。新兴车辆网络中的移动性管理从科学研究和自动驾驶汽车的商业扩张的角度来看都是非常重要的问题。它提出了许多挑战，这些挑战制约着这些网络的采用。事实上，车辆网络将使各种应用成为可能：道路安全应用，高效运输应用，监控应用。

Vehicular networks are emerging networks that make it possible to connect vehicles to one another and to link them with road infrastructures. Managing mobility in such networks raises several challenges we wish to address in this proposal. These networks make it possible to implement different types of applications: safety applications to avoid collisions and road work for example, real-time applications for automated driving assistance, intelligent transport system (ITS) applications to manage traffic and suggest detours amongst others, comfort applications such as automatic toll payments on motorways, connection to online multimedia content, etc. To function efficiently, the vehicle has an increased need for computing power as well as a nearly-continuous connection. The research program we hereby present focuses on intelligent mobility management in emerging vehicular networks. It covers areas as broad as mobile networks, artificial intelligence, machine learning, data science and mathematical optimization, in order to address road congestion created by vehicles and causing traffic jams. The main objective of this research program is to design intelligent methods, algorithms and tools to efficiently manage vehicular mobility in real time through traffic congestion prediction, detection and control. In the longer term, this research program aims at proposing a mobility management architecture for next-generation ad-hoc vehicular networks based on machine learning and endowed with the ability to predict traffic congestion or to reduce it. To achieve this objective, we propose an approach based on the four Vs that characterize big data: Volume, Variety, Veracity and Variability. Our long-term architecture will allow to collect, extract and understand relevant information from network generated data and users in order to infer strategic actions and decisions to detect, control and predict traffic congestion and provide better quality of service as well as an improved experience to road users. Mobility management in emerging vehicular networks is a problem of great importance both from the point of view of scientific research and the commercial expansion in autonomous vehicles. It raises many challenges that condition the adoption of these networks. In fact, vehicular networks will make possible various applications: road safety applications, efficient transport applications, surveillance applications.