Odyssey: 明示的なナビゲーション経路を用いた長期的実世界運転の閉ループベンチマーク
Odyssey: A Closed-Loop Benchmark for Long-Horizon Real-World Driving with Explicit Navigation Routes
100秒間のnuPlanログから再構成した100シナリオで長期的な閉ループ運転を評価するベンチマークを提案し、方向指示の代わりにSDマップ経路を導入して経路追従と車線変更準備を評価する新指標を設けた。
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: Jungho Kim, Hongjae Shin, Seunghoon Yu, Heecheol Yoo, Myeongjun Kim, Jiyong Oh, Donghyuk Kwak, Seunghyeop Nam, Haesung Oh, Hyunju Kim, Hyungchan Cho, Jaehyun Park, Soo Won Seo, Jun Won Choi
分類: cs.RO, cs.AI
原文アブストラクト
Closed-loop evaluation of end-to-end driving requires continuous rollouts that reveal how earlier decisions affect subsequent driving. However, existing benchmarks evaluate only short segments and fail to capture later consequences. Ambiguous directional commands also obscure the intended navigation objective. We introduce Odyssey, a closed-loop benchmark for long-horizon driving comprising 100 scenarios, each reconstructed from a 100-second nuPlan driving log to preserve the context of navigation maneuvers and traffic interactions. To provide a consistent navigation objective, Odyssey replaces directional commands with explicit standard-definition (SD) map routes that specify which roads to follow, while sensor-based planning determines local driving actions. Throughout these rollouts, diffusion-based refinement of 3DGS-rendered images reduces rendering artifacts along the ego trajectory. To assess how effectively planners follow these routes and prepare for upcoming maneuvers, we introduce SD Route Compliance and Pre-Lane Change Score. These assessments are complemented by RouteDS, which extends the Driving Score with penalties for SD-route deviations and failed lane preparation. We adapt state-of-the-art planners, including vision-language-action (VLA) models, and evaluate their navigation performance using these metrics. Odyssey highlights open questions in route representation and integration for E2E driving. Benchmark code and adapted baselines will be released publicly.
関連論文
- 記憶による運転：NAVSIMスコアの再評価自動運転ベンチマーク
- ScenicRules: 多目的仕様と抽象シナリオによる自動運転ベンチマーク自動運転ベンチマーク
- DriveE2E：実世界からシミュレーションへの閉ループ自動運転ベンチマーク自動運転ベンチマーク
- Bench2Drive: 閉ループ自動運転のマルチ能力ベンチマークに向けて自動運転ベンチマーク
- NAVSIM: データ駆動型非反応的自動運転シミュレーションとベンチマーク自動運転ベンチマーク