V2X-QA：自動運転におけるエゴ・インフラ・協調視点を横断するマルチモーダル大規模言語モデルの総合推論データセットとベンチマーク
V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views
自動運転向けMLLMの評価を、車両・インフラ・協調の3視点に分離したプロトコルで行う実世界データセットとベンチマークを構築し、視点依存性や協調推論の課題を明らかにした。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Junwei You, Pei Li, Zhuoyu Jiang, Weizhe Tang, Zilin Huang, Rui Gan, Jiaxi Liu, Yan Zhao, Sikai Chen, Bin Ran
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Multimodal large language models (MLLMs) have shown strong potential for autonomous driving, yet existing benchmarks remain largely ego-centric and therefore cannot systematically assess model performance in infrastructure-centric and cooperative driving conditions. In this work, we introduce V2X-QA, a real-world dataset and benchmark for evaluating MLLMs across vehicle-side, infrastructure-side, and cooperative viewpoints. V2X-QA is built around a view-decoupled evaluation protocol that enables controlled comparison under vehicle-only, infrastructure-only, and cooperative driving conditions within a unified multiple-choice question answering (MCQA) framework. The benchmark is organized into a twelve-task taxonomy spanning perception, prediction, and reasoning and planning, and is constructed through expert-verified MCQA annotation to enable fine-grained diagnosis of viewpoint-dependent capabilities. Benchmark results across ten representative state-of-the-art proprietary and open-source models show that viewpoint accessibility substantially affects performance, and infrastructure-side reasoning supports meaningful macroscopic traffic understanding. Results also indicate that cooperative reasoning remains challenging since it requires cross-view alignment and evidence integration rather than simply additional visual input. To address these challenges, we introduce V2X-MoE, a benchmark-aligned baseline with explicit view routing and viewpoint-specific LoRA experts. The strong performance of V2X-MoE further suggests that explicit viewpoint specialization is a promising direction for multi-view reasoning in autonomous driving. Overall, V2X-QA provides a foundation for studying multi-perspective reasoning, reliability, and cooperative physical intelligence in connected autonomous driving. The dataset and V2X-MoE resources are publicly available at: https://github.com/junwei0001/V2X-QA.
関連論文
- LANTERN: 時間的に接地された警告を用いたVLM協調運転のクローズドループベンチマーク自動運転/ベンチマーク
- nuTruck: 分散電動駆動トラックの自動運転計画のベンチマーク自動運転/ベンチマーク
- 自動運転ネゴシエーター：隠れた意図下での社会的交渉と心の理論のための対話型・検証可能なベンチマーク自動運転/ベンチマーク
- Fail2Drive: クローズドループ運転の汎化性能を評価するベンチマーク自動運転/ベンチマーク
- EagleVision: 高速自動運転レースにおけるクロスドメイン知覚のためのマルチタスクベンチマーク自動運転/ベンチマーク
