Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles
This work addresses the problem of enhancing road safety for connected vehicles by improving perception accuracy, though it is incremental as it builds on existing cooperative perception methods with a focus on network efficiency.
The paper tackles the problem of limited coverage and detection accuracy in individual vehicle perception by proposing a cooperative perception scheme using deep reinforcement learning to select data for transmission, which reduces packet loss and increases detection accuracy by up to 12% compared to a baseline protocol.
Sensor-based perception on vehicles are becoming prevalent and important to enhance the 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 the deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular communication networks and enhances the communication reliability. To design, test, and verify the cooperative perception scheme, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform, which integrates three software components: traffic simulator, vehicle simulator, and object classifier. We evaluate that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.