车路协同作为国际公认的提升道路交通安全与效率的革命性手段，正以场景为驱动逐步实现大范围应用。由于道路交通系统的复杂、随机、时变等特性，尽管车路协同环境下交通要素互联互通，但仍难以实现大范围路网全时全域的状态精准感知以及演化过程的多尺度分析。本项目针对车路协同环境下的大范围路网运行状态节点异质多样、场景复杂时变、数据价值密度高低并存等特点，以移动群体参与节点的自主决策为基础，提出基于博弈论的大范围路网群智感知计算激励机制，研究车路协同环境下的大范围路网运行状态智能协作感知方法，利用深度学习、强化学习网络表达能力、泛化能力强等特性，提取路网运行状态时空特征，针对常态与非常态情况分别构建深层次的路网状态预测与演化组合模型，为车路协同环境下的大范围路网实现敏捷决策和协同优化控制提供重要的支撑。

As an internationally recognized revolutionary means to improve road traffic safety and efficiency, vehicle-to-infrastructure cooperation system has been applied to many situations. Due to the complexity, randomness and time-varying characteristics of traffic system, even the traffic elements are interconnected under vehicle-to-infrastructure cooperation system, the precise prediction and multi-scale evolution analysis of road network state is difficult to realize. Research is performed aiming at the characteristics of large scale road network state under vehicle-to-infrastructure cooperation condition. The incentive mechanism of large scale road network crowd sensing is proposed based on the autonomous decisions of the mobile sensors. The intelligent cooperative perception of large scale road network state under the vehicle-to-infrastructure cooperation could be achieved. By extracting road network spatial and temporal characteristics , the traffic state prediction model and multi-scale evolution model are constructed based on the deep learning and reinforcement learning method. This study could provide an important support for the realization of agile decision-making and optimized and coordinated control for large scale road network under the vehicle-to-infrastructure cooperation condition.