# MultiPark: Multimodal Parking Transformer with Next-Segment Prediction

arXiv: https://arxiv.org/abs/2508.11537 · HTML: https://mlsanity.com/paper/2508.11537/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2025.

## Built on by (most cited first, 4 of 4)

- [A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking](https://mlsanity.com/paper/2510.14000/) (2025)
- [NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments](https://mlsanity.com/paper/2608.24485/) (2026)
- [Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representations](https://mlsanity.com/paper/2609.19894/) (2026)
- [From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking](https://mlsanity.com/paper/2609.24631/) (2026)

## Abstract (arXiv)

Parking accurately and safely in highly constrained spaces remains a critical challenge. Unlike structured driving environments, parking requires executing complex maneuvers such as frequent gear shifts and steering saturation. Recent attempts to employ imitation learning (IL) for parking have achieved promising results. However, existing works ignore the multimodal nature of parking behavior in lane-free open space, failing to derive multiple plausible solutions under the same situation. Notably, IL-based methods encompass inherent causal confusion, so enabling a neural network to generalize across diverse parking scenarios is particularly difficult. To address these challenges, we propose MultiPark, an autoregressive transformer for multimodal parking. To handle paths filled with abrupt turning points, we introduce a data-efficient next-segment prediction paradigm, enabling spatial generalization and temporal extrapolation. Furthermore, we design learnable parking queries factorized into gear, longitudinal, and lateral components, parallelly decoding diverse parking behaviors. To mitigate causal confusion in IL, our method employs target-centric pose and ego-centric collision as outcome-oriented loss across all modalities beyond pure imitation loss. Evaluations on real-world datasets demonstrate that MultiPark achieves state-of-the-art performance across various scenarios. We deploy MultiPark on a production vehicle, further confirming our approach's robustness in real-world parking environments.