V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with
  Multimodal Large Language Models and Graph-of-Thoughts
Current state-of-the-art autonomous vehicles could face safety-critical
situations when their local sensors are occluded by large nearby objects on the
road. Vehicle-to-vehicle (V2V) cooperative autonomous driving has been proposed
as a means of addressing this problem, and one recently introduced framework
for cooperative autonomous driving has further adopted an approach that
incorporates a Multimodal Large Language Model (MLLM) to integrate cooperative
perception and planning processes. However, despite the potential benefit of
applying graph-of-thoughts reasoning to the MLLM, this idea has not been
considered by previous cooperative autonomous driving research. In this paper,
we propose a novel graph-of-thoughts framework specifically designed for
MLLM-based cooperative autonomous driving. Our graph-of-thoughts includes our
proposed novel ideas of occlusion-aware perception and planning-aware
prediction. We curate the V2V-GoT-QA dataset and develop the V2V-GoT model for
training and testing the cooperative driving graph-of-thoughts. Our
experimental results show that our method outperforms other baselines in
cooperative perception, prediction, and planning tasks.
68d2b4940e215259d193b0b2
Hsu-kuang Chiu
claimed_verified
2025-09-24T13:54:20.189Z
68d33b10124bf7d0a4914786
https://cdn-avatars.huggingface.co/v1/production/uploads/68d33b10124bf7d0a4914786/eZQL54niMkBtYFTXoi0HW.jpeg
Hsu-kuang Chiu
user
eddyhkchiu
68d2b4940e215259d193b0b3
Ryo Hachiuma
68d2b4940e215259d193b0b4
Chien-Yi Wang
68d2b4940e215259d193b0b5
Yu-Chiang Frank Wang
68d2b4940e215259d193b0b6
Min-Hung Chen
claimed_verified
2025-09-23T15:15:52.264Z
64ae22dd1aee69ece065cdcd
https://cdn-avatars.huggingface.co/v1/production/uploads/64ae22dd1aee69ece065cdcd/JG7QaHIrr4i2k4uwR4pZK.png
Min-Hung Chen
user
cmhungsteve
68d2b4940e215259d193b0b7
Stephen F. Smith
V2V-GoT: Vehicle-to-Vehicle Cooperative Autonomous Driving with
  Multimodal Large Language Models and Graph-of-Thoughts
Current state-of-the-art autonomous vehicles could face safety-critical
situations when their local sensors are occluded by large nearby objects on the
road. Vehicle-to-vehicle (V2V) cooperative autonomous driving has been proposed
as a means of addressing this problem, and one recently introduced framework
for cooperative autonomous driving has further adopted an approach that
incorporates a Multimodal Large Language Model (MLLM) to integrate cooperative
perception and planning processes. However, despite the potential benefit of
applying graph-of-thoughts reasoning to the MLLM, this idea has not been
considered by previous cooperative autonomous driving research. In this paper,
we propose a novel graph-of-thoughts framework specifically designed for
MLLM-based cooperative autonomous driving. Our graph-of-thoughts includes our
proposed novel ideas of occlusion-aware perception and planning-aware
prediction. We curate the V2V-GoT-QA dataset and develop the V2V-GoT model for
training and testing the cooperative driving graph-of-thoughts. Our
experimental results show that our method outperforms other baselines in
cooperative perception, prediction, and planning tasks.
A graph-of-thoughts framework incorporating occlusion-aware perception and planning-aware prediction enhances cooperative autonomous driving using a Multimodal Large Language Model.
Multimodal Large Language Model graph-of-thoughts occlusion-aware perception planning-aware prediction V2V-GoT-QA dataset V2V-GoT model