MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting
Multi-modal trajectory generation is essential for safe autonomous driving, yet existing diffusion-based planners suffer from high inference latency due to iterative neural function evaluations. This paper presents MISTY (Mixer-based Inference for Single-step Trajectory-drifting Yield), a high-throughput generative motion planner that achieves state-of-the-art closed-loop performance with pure single-step inference. MISTY integrates a vectorized Sub-Graph encoder to capture environment context, a Variational Autoencoder to structure expert trajectories into a compact 32-dimensional latent manifold, and an ultra-lightweight MLP-Mixer decoder to eliminate quadratic attention complexity. Imp...
| Authors | Yining Xing, Zehong Ke, Yiqian Tu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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