AI-Driven Intelligent Vehicle Behavior Decision in Software Defined Internet of Vehicle

Intelligent driving technology plays a key role in reducing road traffic accidents and ensuring driving, where the vehicle behavior decision making capability largely determines the driving performance of intelligent vehicles. At this point, more research is focused on enhancing vehicle environment identification and vehicle control capabilities, with decision-making systems receiving less attention. In order to improve the accuracy of intelligent vehicle behavior decision making and ensure the active safety of path planning, this paper firstly establishes an edge intelligence based software-defined Internet of Vehicles (ESIOV) architecture. Then, a POMDP-based intelligent vehicle behavior decision model is designed using the time-series iterative property of partially observable Markov decision process (POMDP). Finally, a reinforcement learning vehicle behaviour decision (ERVBD) algorithm based on edge intelligence is proposed to ensure the accuracy of intelligent vehicle behavior decision results and improve the decision speed. The simulation results show that ERVBD can successfully implement vehicle behavioural decision making, enabling intelligent vehicles to anticipate collision risks and adopt reasonable avoidance strategies in real time.