Status update frequency optimization for In-vehicle LAN route based on deep Q-learning

Abstract

The popularization of the automated fleet requires a reliable transmission of In-vehicle Local Area Network (LAN) with high mobility under complex environment. Therefore, In-vehicle communication system needs to collect the network status of the vehicles in real time to ensure the availability of routing decisions. The high update frequency of the network status will increase the availability of the routing decisions. However, it will also incur congestion of the In-vehicle LAN. Therefore, it is of great practical interest to optimize the network status update frequency for the In-vehicle LAN to achieve the balance between route availability and network congestion. We consider this important problem in this paper and make the following contributions. Firstly, we analyze the network routing feature information to establish the multi-feature dynamic queue model and age of multi-feature information model, which describes the relationship between the network status and the routing availability. Secondly, this status update frequency selection problem is formulated as a multi-objective constrained optimization and solved by deep Q-learning with feature selection mechanism to improve the timeliness of In-vehicle LAN. Numerical results demonstrate that comparing with the existing algorithms, the drop rate of In-vehicle LAN decreases about 7.68% and the convergence speed of the proposed method improves about 8.3%.