Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations
This work addresses the safety problem for connected and automated vehicles interacting with vulnerable road users, demonstrating a proof-of-concept for urban vehicle automation using only cooperative perception data.
This paper demonstrates cooperative perception (CP) in connected and automated vehicles (CAVs) and intelligent roadside units (IRSUs) to enhance safety for vulnerable road users (VRUs). The experiments show that CAVs can perceive pedestrians around corners and autonomously interact with them using only CP information from IRSUs via vehicle-to-infrastructure (V2I) communication.
Cooperative perception, or collective perception (CP) is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). We present three different experiments to demonstrate the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in the experiments that a connected vehicle (CV) can "see" a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.