Evaluation Is All You Need for Multi-Modal Autonomous Driving
Multi-modal planning is promising for autonomous driving by representing multiple plausible behaviors in ambiguous and long-tail scenarios. Existing methods mainly focus on improving trajectory multi-modality, enhancing trajectory representations, or reshaping the candidate distribution. Nevertheless, we identify a pronounced generation-evaluation asymmetry in multi-modal planning: despite strong oracle performance, existing planners often fail to reliably select the best available candidate, leaving substantial planning potential unrealized. To address this challenge, we propose iDriveVLA, a multi-modal planning framework that improves the candidate trajectory space while enabling more r...
| Authors | Zeyu He, Shiqi Liu, Ke Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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