REVIEW 6 cited by
LangCoop: Collaborative Driving with Language
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Multi-agent collaboration holds great promise for enhancing the safety, reliability, and mobility of autonomous driving systems by enabling information sharing among multiple connected agents. However, existing multi-agent communication approaches are hindered by limitations of existing communication media, including high bandwidth demands, agent heterogeneity, and information loss. To address these challenges, we introduce LangCoop, a new paradigm for collaborative autonomous driving that leverages natural language as a compact yet expressive medium for inter-agent communication. LangCoop features two key innovations: Mixture Model Modular Chain-of-thought (M$^3$CoT) for structured zero-shot vision-language reasoning and Natural Language Information Packaging (LangPack) for efficiently packaging information into concise, language-based messages. Through extensive experiments conducted in the CARLA simulations, we demonstrate that LangCoop achieves a remarkable 96\% reduction in communication bandwidth (< 2KB per message) compared to image-based communication, while maintaining competitive driving performance in the closed-loop evaluation. Our project page and code are at https://xiangbogaobarry.github.io/LangCoop/.
Forward citations
Cited by 6 Pith papers
-
Unleashing Hierarchical Reasoning: An LLM-Driven Framework for Training-Free Referring Video Object Segmentation
A training-free pipeline parses a video query with an LLM, tracks candidates with SAM2, then uses coarse motion plus conditional pose reasoning to segment the referred object.
-
AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration
AirV2X-Perception is a 6.73-hour simulated dataset and benchmark for collaborative perception with up to 5 vehicles, 5 roadside units, and 5 drones.
-
CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning
Post-episode multi-agent debriefing lets LLM driving agents learn concise natural-language coordination protocols that avoid collisions and merge traffic, and distillation makes the policy fast enough for near-real-time use.
-
A Survey on Vision-Language-Action Models for Autonomous Driving
A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.
-
Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
A survey that classifies chain-of-thought methods for autonomous driving into modular, logical, and reflective pipelines, and proposes three evolutionary stages from direct prompting to reinforcement learning.
-
Automated Vehicles Should be Connected with Natural Language
A vision paper recommending natural language as the universal communication medium for connected and automated vehicles.
Discussion (0). Sign in to comment.