A DRL based 4-r Computation Model for Object Detection on RSU using LiDAR in IloT

Abstract

Internet of vehicle (IoV) network comprises Road Side Unit (RSU), which has become a computation and communication device for effective LiDAR data communication (ex: object detect information) between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle. However, the LiDARs generate a massive volume of 3D data with a notable redundancy rate leads to inadequate object detection accuracy, and the high operational cost of RSU due to inadequate resource and time consumption. Estimating the computation capacity for RSU selection is an NP-hard problem. To address this issue, we propose a Deep Reinforcement Learning (DRL) influenced 4-r computation model to measure RSU cost based on resource feasibility factor and object region detection rate based on novel region-of-interest (RoI) strategy. The resource feasibility factor appraises the residual capacity and cost of RSU based on a criterion of optimality. The RoI strategy eliminates irrelevant points, noise and ground points based on distance and shape measures of an object on RSU with feasible consumption of computation resources. The simulation results show that our mechanism achieves 83% average object detection accuracy rate, 81% average service rate and 17% service offloading rate than state-of-art approaches.