Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement

• URL: http://arxiv.org/abs/2509.20938v1
• Date: Thu, 25 Sep 2025 09:24:45 GMT
• Title: Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement
• Authors: Jianbo Zhao, Taiyu Ban, Xiangjie Li, Xingtai Gui, Hangning Zhou, Lei Liu, Hongwei Zhao, Bin Li,
• Abstract summary: Autoregressive models are a formidable baseline for end-to-end planning in autonomous driving.<n>Their performance is constrained by atemporal misalignment, as the planner must condition future actions on past sensory data.<n>We propose a Time-Invariant Alignment (TISA) module that learns to project initial environmental features into a consistent ego-centric frame.<n>We also introduce a multi-objective post-training stage using Direct Preference Optimization (DPO) to move beyond pure imitation.
• Score: 15.002921311530374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The inherent sequential modeling capabilities of autoregressive models make them a formidable baseline for end-to-end planning in autonomous driving. Nevertheless, their performance is constrained by a spatio-temporal misalignment, as the planner must condition future actions on past sensory data. This creates an inconsistent worldview, limiting the upper bound of performance for an otherwise powerful approach. To address this, we propose a Time-Invariant Spatial Alignment (TISA) module that learns to project initial environmental features into a consistent ego-centric frame for each future time step, effectively correcting the agent's worldview without explicit future scene prediction. In addition, we employ a kinematic action prediction head (i.e., acceleration and yaw rate) to ensure physically feasible trajectories. Finally, we introduce a multi-objective post-training stage using Direct Preference Optimization (DPO) to move beyond pure imitation. Our approach provides targeted feedback on specific driving behaviors, offering a more fine-grained learning signal than the single, overall objective used in standard DPO. Our model achieves a state-of-the-art 89.8 PDMS on the NAVSIM dataset among autoregressive models. The video document is available at https://tisa-dpo-e2e.github.io/.

Related papers

• ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving [40.28153843744977]
  We propose Temporal Residual World Model (TR-World), which focuses on dynamic object modeling.<n>By calculating the temporal residuals of scene representations, the information of dynamic objects can be extracted without relying on detection and tracking.<n>We also propose Future-Guided Trajectory Refinement (FGTR) module, which conducts interaction between prior trajectories and the future BEV features.
  arXiv  Detail & Related papers  (2026-02-11T14:12:26Z)
• Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving [54.46325690390831]
  We propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment.<n>MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine.<n>MPA trains a diffusion-based policy adapter to refine the base policy's predictions and a multi-step Q value model to evaluate long-term outcomes.
  arXiv  Detail & Related papers  (2025-11-26T17:01:41Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Reinforced Reasoning for Embodied Planning [18.40186665383579]
  Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and natural language goals.<n>We introduce a reinforcement fine-tuning framework that brings R1-style reasoning enhancement into embodied planning.
  arXiv  Detail & Related papers  (2025-05-28T07:21:37Z)
• Deep Active Inference Agents for Delayed and Long-Horizon Environments [1.693200946453174]
  AIF agents rely on accurate immediate predictions and exhaustive planning, a limitation that is exacerbated in delayed environments.<n>We propose a generative-policy architecture featuring a multi-step latent transition that lets the generative model predict an entire horizon in a single look-ahead.<n>We evaluate our agent in an environment that mimics a realistic industrial scenario with delayed and long-horizon settings.
  arXiv  Detail & Related papers  (2025-05-26T11:50:22Z)
• Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies [9.639797094021988]
  World Model-based Reinforcement Learning (WMRL) enables sample efficient policy learning.<n>We propose Imagine-2-Drive, a novel WMRL framework that integrates a high-fidelity world model with a multi-modal diffusion-based policy actor.<n>By training DPA within DiffDreamer, our method enables robust policy learning with minimal online interactions.
  arXiv  Detail & Related papers  (2024-11-15T13:17:54Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.