LACO: Adaptive Latent Communication for Collaborative Driving

TL;DR

LACO introduces an adaptive latent communication framework for collaborative driving that reduces latency and maintains performance by leveraging pretrained models and novel reasoning and information selection techniques.

Contribution

It proposes a training-free latent communication paradigm with techniques like ILD, CHSA, and SSKD to improve multi-agent collaborative driving.

Findings

LACO reduces communication and inference latency significantly.

LACO maintains strong driving performance in CARLA simulations.

The approach effectively addresses agent identity confusion in latent communication.

Abstract

Collaborative driving aims to improve safety and efficiency by enabling connected vehicles to coordinate under partial observability. Recent approaches have evolved from sharing visual features for perception to exchanging language-based reasoning through foundation models for behavioral coordination. Though communicating in language provides intuitive information, it introduces two challenges: high latency caused by autoregressive decoding and information loss caused by compressing rich internal representations into discrete tokens. To address these challenges, we analyze latent communication in collaborative driving under inherent limitations of multi-agent settings. Our analysis reveals agent identity confusion, where direct fusion of latent states entangles decision representations across vehicles. Motivated by this, we propose LACO, a training-free \textbf{LA}tent \textbf{CO}mmunication paradigm that seamlessly adapts pretrained driving models to collaborative settings. LACO introduces Iterative Latent Deliberation (ILD) for latent reasoning, Cross-Horizon Saliency Attribution (CHSA) for communication-efficient information selection, and Structured Semantic Knowledge Distillation (SSKD) to stabilize ego-centric decision making. Closed-loop experiments in CARLA show that LACO notably reduces communication and inference latency while maintaining strong collaborative driving performance.