Adaptive Content Control for Communication amongst Cooperative Automated Vehicles

TL;DR

This paper introduces an adaptive communication framework for cooperative automated vehicles that uses multi-resolution maps and probabilistic strategies to optimize information exchange and extend environmental awareness.

Contribution

It proposes a scalable, adaptive communication method based on multi-resolution maps and probabilistic control to improve vehicle cooperation.

Findings

Enhanced environmental awareness beyond sensor range

Adaptive message control reduces communication load

Improved decision-making in automated driving

Abstract

Cooperative automated vehicles exchange information to assist each other in creating a more precise and extended view of their surroundings, with the aim of improving automated-driving decisions. This paper addresses the need for scalable communication among these vehicles. To this end, a general communication framework is proposed through which automated cars exchange information derived from multi-resolution maps created using their local sensing modalities. This method can extend the region visible to a car beyond the area directly sensed by its own sensors. An adaptive, probabilistic, distance-dependent strategy is proposed that controls the content of the messages exchanged among vehicles based on performance measures associated with the load on the communication channel.