MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting

TL;DR

MISTY is a high-throughput, single-step motion planner for autonomous driving that combines a vectorized encoder, VAE, and lightweight decoder to generate proactive maneuvers rapidly and robustly.

Contribution

MISTY introduces a novel single-step diffusion-based motion planning approach with a latent-space drifting loss, enabling fast and proactive trajectory generation.

Findings

Achieves state-of-the-art performance on nuPlan benchmark.

Operates at over 99 FPS with 10.1 ms latency.

Generates proactive maneuvers like overtaking absent in raw data.

Abstract

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. Importantly, we introduce a latent-space drifting loss that shifts the complex distribution evolution entirely to the training phase. By formulating explicit attractive and repulsive forces, this mechanism empowers the model to synthesize novel, proactive maneuvers, such as active overtaking, that are virtually absent from the raw expert demonstrations. Extensive evaluations on the nuPlan benchmark demonstrate that MISTY achieves state-of-the-art results on the challenging Test14-hard split, with comprehensive scores of 80.32 and 82.21 in non-reactive and reactive settings, respectively. Operating at over 99 FPS with an end-to-end latency of 10.1 ms, MISTY offers an order-of-magnitude speedup over iterative diffusion planners while while achieving significantly robust generation.