Resource Optimization for Task Offloading With Real-Time Location Prediction in Pedestrian-Vehicle Interaction Scenarios

With the development of autonomous driving, task offloading of Internet of vehicles has become a hot research issue. In pedestrian-vehicle interaction scenarios, characteristics of tasks are constantly changing due to the influence of pedestrians and road conditions. Real-time offloading optimization and signaling are time-consuming, which may not meet the low delay requirement of task offloading. Therefore, this paper proposes a location prediction-based resource optimization scheme for task offloading in these scenarios. Firstly, the locations of pedestrians are predicted by the social force model based on their movement rules, and the locations of vehicles are predicted by the car-following model on the basis of ensuring pedestrian safety. The characteristics of tasks are obtained based on the predicted locations of vehicles. Then a neural network trained beforehand based on deep Q-learning is used to obtain a task offloading strategy. Since the tasks are obtained by prediction in advance, this strategy decision can be processed before vehicles arriving the predicted locations, which saves the time consumption of optimization and signaling. Besides, simulation results show that the proposed scheme still guarantees an acceptably low task offloading delay compared with the other methods, especially in congested areas.