V2V-GoT-QA: Multimodal QA for Cooperative Autonomous Driving

110,000 training and 31,000 testing question-answer pairs for multimodal LLM-based autonomous driving. The dataset includes nine types of tasks across occlusion-aware perception, planning-aware prediction, and V2V-aware planning stages. It was created by eddyhkchiu and last updated on Hugging Face in February 2026.

Use Cases

• Training multimodal LLMs for occlusion-aware object detection based on the described perception tasks (Q1-Q4).
• Evaluating graph-of-thoughts reasoning for vehicle-to-vehicle (V2V) cooperative planning based on the described V2V-aware planning tasks.
• Benchmarking model performance on planning-aware trajectory prediction based on the described prediction tasks (Q5-Q7).

Strengths

• Contains 141,000 total QA pairs (110K training, 31K testing), providing a substantial benchmark.
• Covers nine distinct task types across three core autonomous driving stages (perception, prediction, planning).
• Designed for graph-of-thoughts reasoning, a specific and advanced evaluation framework.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the full dataset is unknown, which may limit suitability assessment.
• Data may reflect biases inherent to its unspecified collection sources and simulation environment.

Provenance

Source

Hugging Face user eddyhkchiu.

Collection Method

Method of data gathering is not specified in the provided description.

Freshness

Last updated 2026-02-17 17:01:42; freshness should be verified.