Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the synergy between connected vehicles and the edge of the network infrastructure, in order to envision global traffic policies that can effectively drive local decisions. Local decisions concern individual vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when; global decisions, instead, involve whole traffic flows. Such decisions are made at different time scales by different entities, which are integrated within an edge-based architecture and can share information. In particular, we leverage a queuing-based model and formulate an optimization problem to make global decisions on traffic flows. To cope with the problem complexity, we then develop an iterative, linear-time complexity algorithm called Bottleneck Hunting (BH). We show the performance of our solution using a realistic simulation framework, integrating a Python engine with ns-3 and SUMO, and considering two relevant services, namely, lane change assistance and navigation, in a real-world scenario. Results demonstrate that our solution leads to a reduction of the vehicles' travel times by 66 in the case of lane change assistance and by 20 for navigation, compared to traditional, local-coordination approaches.
翻译:协助驾驶相联汽车是5G和超越网络网络所应支持的主要应用之一。在这项工作中,我们提出一个协助驾驶系统,利用连接车辆与网络基础设施边缘之间的协同作用,以设想能够有效推动当地决策的全球交通政策。地方决定涉及个别车辆,例如,哪部车辆应进行车道改变机动,何时进行;全球决定涉及整个交通流动。这些决定是由不同实体在不同时间尺度上作出的,这些实体被纳入边基建筑,可以分享信息。特别是,我们利用一个基于排队的模式,并拟订一个优化的问题,就交通流动作出全球决定。为了应对问题的复杂性,我们随后开发一个迭代的、线性复杂算法,称为博特内克狩猎(BH)。我们利用一个现实的模拟框架,将Python发动机与ns-3和SUMO结合起来,并考虑在现实世界情景下提供两个相关服务,即路道改变援助和导航。结果显示,我们的解决办法导致减少车辆的行驶次数,通过传统的航道调整方式,通过传统的导航到20个路段,通过传统的导航调整来显示我们的解决办法。