Intelligent mobility management in emerging vehicular networks

车辆网络是新兴的网络，它使车辆之间的连接成为可能，并将它们与道路基础设施连接起来。管理此类网络中的移动性带来了我们希望在本提案中解决的几个挑战。这些网络使实现不同类型的应用成为可能：例如，用于避免碰撞和道路作业的安全应用、用于自动驾驶辅助系统的实时应用、用于管理交通和建议绕行的智能交通系统(ITS)应用、诸如高速公路自动通行费支付的舒适性应用、在线多媒体内容连接等。为了有效地发挥作用，车辆对计算能力以及几乎不间断的连接的需求越来越大。我们在此提出的研究计划集中在新兴车载网络中的智能移动性管理。它涵盖了移动网络、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造成的交通拥堵。该研究项目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、检测和控制来有效地实时管理车辆的机动性。从长远来看，该研究计划旨在为下一代自组织车载网络提出一种基于机器学习的移动性管理体系结构，并赋予其预测或缓解交通拥堵的能力。为了实现这一目标，我们提出了一种基于大数据特征的四个V：数量、多样性、准确性和可变性的方法。我们的长期架构将允许从网络生成的数据和用户中收集、提取和了解相关信息，以便推断战略行动和决策，以检测、控制和预测交通拥堵，并为道路用户提供更好的服务质量和更好的体验。无论是从科学研究的角度还是从自动驾驶汽车的商业扩展的角度来看，新兴车载网络中的移动性管理都是一个非常重要的问题。这带来了许多挑战，这些挑战制约了这些网络的采用。事实上，车载网络将使各种应用成为可能：道路安全应用、高效交通应用、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.