Reading
V2V-GoT is the first Graph-of-Thoughts reasoning framework designed specifically for vehicle-to-vehicle (V2V) collaborative autonomous driving. It integrates multi-vehicle perception information via multimodal large language models to achieve occluded perception and planning-aware prediction, outperforming baseline methods in collaborative perception, prediction, and planning tasks.
Section 01
V2V-GoT is the first Graph-of-Thoughts reasoning framework designed specifically for vehicle-to-vehicle (V2V) collaborative autonomous driving. It integrates multi-vehicle perception information via multimodal large language models to achieve occluded perception and planning-aware prediction, outperforming baseline methods in collaborative perception, prediction, and planning tasks.
Section 02
One of the core bottlenecks of autonomous driving technology lies in the physical limitations of single-vehicle perception systems, where occlusion issues easily lead to safety hazards. Vehicle-to-vehicle (V2V) communication can expand the field of view, but traditional methods use simple feature fusion, making it difficult to leverage semantic correlations of multi-source information and perform complex reasoning.
Section 03
V2V-GoT introduces Graph-of-Thoughts structured reasoning, decomposed into associated QA nodes. Two key innovations: 1. Occluded perception: Identify occluded areas and infer occluded targets using information from other vehicles; 2. Planning-aware prediction: Predict the behavior of other participants by combining the ego vehicle's candidate trajectories.
Section 04
Constructed the V2V-GoT-QA dataset (based on V2V4Real, including multi-vehicle perception features and QA sequences); fine-tuned using LoRA technology on LLaVA 1.5 for 10 epochs to adapt to the V2V collaborative domain.
Section 05
Outperforms baselines in collaborative perception, prediction, and planning tasks, with significant advantages in occlusion scenarios; Graph-of-Thoughts provides interpretability, allowing traceable reasoning paths and easy integration of domain knowledge such as traffic rules.
Section 06
The project is open-source (GitHub includes code, datasets, etc., with datasets hosted on Hugging Face); future extensions can include vehicle-to-infrastructure (V2I) and multimodal sensor fusion scenarios, providing an example for combining large model reasoning with physical perception.
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