Sharing compressed latent states and planned waypoints between agents, triggered by prediction errors, improves multi-agent driving performance in CARLA while cutting communication bandwidth by roughly 50x.
The BEV representation can be learnt by using algorithms such as BevFusion Liu et al
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Ego-centric Learning of Communicative World Models for Autonomous Driving
Sharing compressed latent states and planned waypoints between agents, triggered by prediction errors, improves multi-agent driving performance in CARLA while cutting communication bandwidth by roughly 50x.