DriftParking: Trajectory Modeling via Drifting Field for End-to-End Automated Parking

• URL: http://arxiv.org/abs/2609.06923v1
• Date: Mon, 07 Sep 2026 01:42:57 GMT
• Title: DriftParking: Trajectory Modeling via Drifting Field for End-to-End Automated Parking
• Abstract summary: We propose DriftParking, a one-step trajectory generation framework.<n>We replace distribution-level attraction with conditional one-to-one attraction toward the paired expert trajectory.<n>We decompose each trajectory into a start-to-goal baseline and a learnable residual, turning endpoint alignment into a representation-level structural constraint.
• Score: 16.779595425807457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated parking requires generating complete and executable trajectories in highly constrained spaces with low tolerance for goal pose error. Existing end-to-end parking methods struggle to jointly achieve inference efficiency, trajectory quality, and precise endpoint alignment, while conventional imitation objectives provide limited supervision on structured deviations from expert maneuver geometry. We propose DriftParking, a one-step trajectory generation framework that reconstructs the drifting-field paradigm for high-precision conditional trajectory generation. Specifically, we replace distribution-level attraction with conditional one-to-one attraction toward the paired expert trajectory, introduce expert-centered constructive repulsion, and adaptively attenuate repulsion near convergence. We further formulate trajectory generation in an endpoint-residual space by decomposing each trajectory into a start-to-goal baseline and a learnable residual, turning endpoint alignment into a representation-level structural constraint on the supervision target while providing a structured space for repulsive supervision. DriftParking achieves state-of-the-art performance across all evaluation metrics. Closed-loop on-vehicle experiments across diverse parking scenarios further show a 97% parking success rate, demonstrating strong zero-shot generalization.

Related papers

• NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments [25.61185429754509]
  We present NeuralParker, a reinforcement learning-based hybrid planner for arbitrary-pose parking.<n>It encodes full-environment obstacle and boundary geometry in a target-relative representation.<n>It couples a learned curvature--length arc policy with an in-loop terminal ensemble that selects from diverse cubic Hermite connections.
  arXiv  Detail & Related papers  (2026-08-25T12:33:14Z)
• Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning [60.888463449435484]
  We present Shift & Drift, a novel dual-track benchmark designed to rigorously stress-test motion planners across two critical axes of distribution shift.<n>We show that imitation learning methods achieve high scores in ID benchmarks, but suffer from persistent drift when subjected to temporally correlated actuation noise.<n>Our findings reveal an empirical trade-off between imitation fidelity and closed-loop resilience, providing the community with a rigorous benchmark to evaluate progress toward reliable deployment.
  arXiv  Detail & Related papers  (2026-07-08T18:23:48Z)
• Drift-Resistant Navigation World Model with Anchored Epipolar Guidance [81.52175010905665]
  We propose Drift-Resistant Navigation World Model, a generative model that mitigates both perceptual drift and geometric drift in conventional rollout-based navigation world models.<n>We first predict sparse future anchors that serve as stable long-range targets, and then generate intermediate frames within each chunk conditioned on both past context and future anchors.<n>Experiments on four benchmarks demonstrate consistent improvements over strong baselines in long-horizon visual quality, geometric consistency, and multi-view coherence.
  arXiv  Detail & Related papers  (2026-05-23T22:47:33Z)
• FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving [9.654458127972736]
  FeaXDrive is a feasibility-aware trajectory-centric diffusion planning method for end-to-end autonomous driving.<n>Built on this trajectory-centric formulation, FeaXDrive integrates adaptive curvature-constrained training to improve intrinsic geometric and kinematic feasibility.<n>Experiments on the NAVSIM benchmark show that FeaXDrive achieves strong closed-loop planning performance.
  arXiv  Detail & Related papers  (2026-04-14T12:24:54Z)
• SunnyParking: Multi-Shot Trajectory Generation and Motion State Awareness for Human-like Parking [24.480545421791234]
  We propose a novel dual-branch E2E architecture that achieves motion state awareness by jointly predicting spatial trajectories and discrete motion state sequences.<n> Experimental results demonstrate that our framework generates more robust and human-like trajectories in complex multi-shot parking scenarios.<n>We open-source a new parking dataset of the CARLA simulator, specifically designed to evaluate full prediction capabilities under complex maneuvers.
  arXiv  Detail & Related papers  (2026-02-25T08:35:58Z)
• Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing [74.83272587893508]
  We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction.
  arXiv  Detail & Related papers  (2026-02-17T15:10:44Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• FlowDrive: Energy Flow Field for End-to-End Autonomous Driving [50.89871153094958]
  FlowDrive is a novel framework that introduces physically interpretable energy-based flow fields to encode semantic priors and safety cues into the BEV space.<n> Experiments on the NAVSIM v2 benchmark demonstrate that FlowDrive achieves state-of-the-art performance with anS of 86.3, surpassing prior baselines in both safety and planning quality.
  arXiv  Detail & Related papers  (2025-09-17T13:51:33Z)
• EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving [17.57364638932072]
  EvaDrive is a novel reinforcement learning framework for autonomous driving.<n>It provides a closed-loop adversarial framework for human-like iterative decision-making.<n>Extensive experiments on NAVSIM and Bench2Drive benchmarks demonstrate SOTA performance.
  arXiv  Detail & Related papers  (2025-08-05T11:26:28Z)
• ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models [6.58562706945347]
  ParkDiffusion is a novel approach that predicts the trajectories of both vehicles and pedestrians in automated parking scenarios.<n>ParkDiffusion employs diffusion models to capture the inherent uncertainty and multi-modality of future trajectories.<n>We evaluate ParkDiffusion on the Dragon Lake Parking dataset and the Intersections Drone dataset.
  arXiv  Detail & Related papers  (2025-05-01T15:16:59Z)
• 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)

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.