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
Self-Driving Negotiator: An interactive, verifiable benchmark for social negotiation and theory of mind under hidden intent
自動運転における暗黙の社会的交渉を評価する、テキストベースのマルチターン環境を提案し、モデルの性能を検証した。
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: Ashutosh Kumar
分類: cs.GT, cs.RO
原文アブストラクト
Autonomous driving is full of tiny social negotiations: a driver presses forward, another yields, a pedestrian fakes toward the curb, or a lane vehicle chooses whether to open a merge gap. Such interactions require inferring hidden intent from behavior under partial observability and then acting safely and efficiently. Existing autonomous-driving language benchmarks mostly focus on perception, visual question answering, or open-loop planning, while existing language-agent negotiation benchmarks typically make the negotiation explicit in text. Self-Driving Negotiator bridges the gap between the two: a text-only, multi-turn, procedurally generated environment for measuring implicit social coordination in driving. Agents generate specific driving actions. Reward and diagnostics are computed from the privileged simulator state, not from the explanation of the model. This report covers task design, reward and anti-gaming invariants, validated scenarios, non-LLM baselines, and a six-model inference leaderboard. Current models are far removed from the scripted expert. The best average success rate across three scenarios is 0.68; contested merge is statistically flat across models; and difficulty tiers separate cue-following from true wait-for-commitment behavior.
