Intelligent Transportation Vehicle Road Collaboration and Task Scheduling Based on Deep Learning in Augmented Internet of Things

With the continuous development of intelligent transportation systems, achieving efficient vehicle-road coordination and task scheduling has become increasingly important for enhancing safety and efficiency. In order to effectively solve the problem that it is difficult to adapt to the intelligent transportation system due to the heterogeneity of cloud and edge computing resources and the complexity of communication, this paper proposes an intelligent transportation vehicle road collaboration and task scheduling method based on deep learning in the enhanced Internet of Things (IoT) environment. The proposed method utilizes a Collaborative Task Placement Deep Reinforcement Learning strategy (CTPDRL) and builds a cloud-edge collaborative computing framework controlled by deep reinforcement learning. Then, from the perspective of the interests of users and service providers, the task model under cloud edge collaborative computing is analyzed, and a system service quality model is established. Through Deep Q Networks (DQN) and Q-tables, CTPDRL can optimize the allocation of computing and communication resources, achieving effective task placement. The experimental results show that our proposed method can effectively reduce the overall cost of cloud and edge cloud computing, improve the practicality of the system in multi edge cloud environments, and help solve the problem of image perception blind spots in intelligent transportation, thereby improving the safety and efficiency of intelligent driving.