PCLA-Bridge: CARLAエージェントのクロスドメイン展開フレームワーク
PCLA-Bridge: A Framework for Cross-Domain Deployment of CARLA Agents
CARLA Leaderboard向けエージェントを改変なしで他シミュレータや実データに展開できるツールを提案し、nuScenesや3種のシミュレータで動作を検証した。
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: Masoud Jamshidiyan Tehrani, Jinhan Kim, Paolo Tonella
分類: cs.SE, cs.RO
原文アブストラクト
Most high-performing end-to-end autonomous driving agents (ADAs) are developed for the CARLA Leaderboard, tightly coupling them to CARLA's APIs, sensor layouts, coordinate systems, and internal clock. Reusing these models on real datasets, alternative simulators, or physical platforms necessitates substantial manual re-engineering. We present PCLA-Bridge, a deployable tool that runs unmodified CARLA Leaderboard agents from any sensor source to any control destination without an active CARLA server. With PCLA-Bridge handling dependency isolation, tensor marshalling, routing, and synthetic timing, integrators only need to provide a minimal interface. We validate the tool across two operational paradigms: offline replay on in-domain CARLA recordings and real-world nuScenes logs, and live closed-loop control across three diverse simulation engines: MetaDrive, AWSIM, and AlpaSim. We analyze the lightweight integration requirements for each platform, highlighting output translation (such as converting agent actions into target trajectory formats) and coordinate-frame transformations, showing that integration requires minimal glue code rather than structural changes. All eight evaluated agents execute across all three simulators without modification, confirming the reliability of PCLA-Bridge and establishing a foundation for systematic sim-to-real, cross-sim, and security testing.
