Robustifying cooperative awareness in autonomous vehicles through local information diffusion

Cooperative Intelligent Transportation Systems envision
the integration of cooperative intelligence as a key operational
part of autonomous driving. In this way, a fleet or swarm
of Connected and Automated Vehicles collectively coordinates its
driving actions in order to maximize its performance. To realize
this ambition, vehicles need to be fully location-aware of their
surrounding environment, through distributed AI intelligence.
Motivated by this requirement, we develop in this paper a
distributed cooperative awareness scheme which performs multimodal
fusion of heterogeneous sensor sources along with V2V
communication information, using graph Laplacian matrix and
Least-Mean-Squares algorithm. The intuition behind our approach
is that neighboring vehicles are interested in estimating
common positions of other vehicles. We build upon our previous
work on global awareness though local information diffusion, and
prove that the proposed distributed framework is able to address
highly efficient the case of lacking any information about other
networked vehicles. More specifically, our approach achieves high
enough convergence speed as well as location accuracy. The
evaluation study has been performed in CARLA autonomous
driving simulator and verifies the proposed method’s benefits
over other related solutions.