Drive-P2D: 自動運転向けVLMのための段階的知覚から意思決定へのベンチマーク
Drive-P2D: A Progressive Perception-to-Decision Benchmark for VLMs in Autonomous Driving
自動運転における視覚言語モデル(VLM)を、物体・シーン・意思決定の3段階で評価する6,650問のベンチマークを提案し、推論と回答を分離して採点・誤り分析を行う。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zecong Tang, Zixu Wang, Yifei Wang, Weitong Lian, Tianjian Gao, Haoran Li, Tengju Ru, Lingyi Meng, Zhejun Cui, Yichen Zhu, Qi Kang, Kaixuan Wang, Yu Zhang
分類: cs.AI, cs.CV, cs.RO
原文アブストラクト
Autonomous driving requires reliable perception and safe decision-making in complex scenarios. Recent vision-language models (VLMs) demonstrate reasoning and generalization abilities, opening new possibilities for autonomous driving; however, existing benchmarks often evaluate perception and decision-making separately, limit failure analysis with choice-only formats, or introduce evaluation bias through LLM-scored long-form outputs. To address these issues, we present Drive-P2D, a progressive perception-to-decision benchmark with 6,650 questions across Object, Scene, and Decision levels. Drive-P2D adopts a separated reasoning-and-answer protocol: final answers are scored objectively, while reasoning is analyzed to identify error modes exposed along the progressive perception-to-decision chain. We evaluate mainstream VLMs across all and high-risk scenarios, and further characterize the perception-to-decision capability boundary through correlation analysis and similar-scene robustness testing. Reasoning further exposes failure modes such as logical reasoning errors and semantic feature omissions, and we train a lightweight analyzer model to automate large-scale error-mode annotation of reasoning. Together, these designs provide practical insights for building safer and more reliable VLMs for real-world autonomous driving.
