  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.
