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首页> 外文会议>2018 International Conference on Intelligent Systems and Computer Vision >Deep neural network dynamic traffic routing system for vehicles
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Deep neural network dynamic traffic routing system for vehicles

机译：车辆的深度神经网络动态交通路由系统

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摘要

Traffic grids have always suffered from a lack of dynamic routing and path planning algorithms and relied only on static characteristics of the roads like the number of lanes, distance and speed limits to avoid and resolve traffic congestion, by routing traffic to a lighter traffic path. However, with the increased number of vehicles in urban areas these algorithms may have reached their limitation due to the huge increase in the state space in a limited computing power and memory environment. In this research we will introduce a dynamic routing system for traffic in intersections based on real-time traffic conditions such as individual vehicle speed, destination and traffic light status to provide the fasted path between a source and a target point. This system will exploit the recent advancements in the field of machine learning by leveraging the power of deep learning especially deep convolutional neural networks. Simulation shows that the proposed model results in a path that are generally fast and avoids frequent red light stops.
机译：交通网格一直缺乏动态路由和路径规划算法，仅依靠道路的静态特性（例如车道数量，距离和速度限制）来避免和解决交通拥堵，方法是将交通路由到较轻的交通路径。然而，随着城市地区车辆数量的增加，由于在有限的计算能力和存储环境中状态空间的巨大增加，这些算法可能已达到其局限性。在这项研究中，我们将基于实时交通状况（例如单个车速，目的地和交通灯状态）为十字路口的交通引入动态路由系统，以提供源点与目标点之间的快速路径。该系统将利用深度学习（尤其是深度卷积神经网络）的强大功能，利用机器学习领域的最新进展。仿真表明，提出的模型产生的路径通常较快，并且避免了频繁的红灯停止。

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