PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene tokens into a coupled representation space, forcing a single planning branch to jointly model agent interaction, road geometry, and driving intention. Such coupling may weaken factor-specific reasoning and obscure the contribution of different planning cues. To address this limitation, PrismAD partitions scene tokens into interaction, geometry, and intent groups, and assigns them to independent planning experts with the same architecture but separate parameters. Each expert learns a specialized motion-planning re...
| Authors | Kang Ding, Zhigui Lin, Hongsong Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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