Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles
Shunsuke Aoki, Takamasa Higuchi, Onur Altintas
Abstract
Sensor-based perception on vehicles are becoming prevalent and important to enhance road safety. Autonomous driving systems use cameras, LiDAR and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. By using deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular networks and enhances the communication reliability. To design, test and verify the practical and resource-efficient cooperative perception framework, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform where we integrate three software components: a traffic simulator, a vehicle simulator, and an object classifier. The simulation platform constitutes a unified framework to evaluate a traffic model, vehicle model, communication model, and object classification model. Simulation results show that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.