HiMAP: History-aware Map-occupancy Prediction with Fallback

• URL: http://arxiv.org/abs/2602.17231v1
• Date: Thu, 19 Feb 2026 10:24:02 GMT
• Title: HiMAP: History-aware Map-occupancy Prediction with Fallback
• Authors: Yiming Xu, Yi Yang, Hao Cheng, Monika Sester,
• Abstract summary: We present textbfHiMAP, a prediction framework that remains reliable under MOT failures.<n>HiMAP converts past detections into historical occupancy maps and retrieves agent-specific history from unlabeled occupancy representations.<n>On Argoverse2, HiMAP performance comparable to tracking-based methods while operating without IDs.
• Score: 17.3826007449443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate motion forecasting is critical for autonomous driving, yet most predictors rely on multi-object tracking (MOT) with identity association, assuming that objects are correctly and continuously tracked. When tracking fails due to, e.g., occlusion, identity switches, or missed detections, prediction quality degrades and safety risks increase. We present \textbf{HiMAP}, a tracking-free, trajectory prediction framework that remains reliable under MOT failures. HiMAP converts past detections into spatiotemporally invariant historical occupancy maps and introduces a historical query module that conditions on the current agent state to iteratively retrieve agent-specific history from unlabeled occupancy representations. The retrieved history is summarized by a temporal map embedding and, together with the final query and map context, drives a DETR-style decoder to produce multi-modal future trajectories. This design lifts identity reliance, supports streaming inference via reusable encodings, and serves as a robust fallback when tracking is unavailable. On Argoverse~2, HiMAP achieves performance comparable to tracking-based methods while operating without IDs, and it substantially outperforms strong baselines in the no-tracking setting, yielding relative gains of 11\% in FDE, 12\% in ADE, and a 4\% reduction in MR over a fine-tuned QCNet. Beyond aggregate metrics, HiMAP delivers stable forecasts for all agents simultaneously without waiting for tracking to recover, highlighting its practical value for safety-critical autonomy. The code is available under: https://github.com/XuYiMing83/HiMAP.

Related papers

• PredMapNet: Future and Historical Reasoning for Consistent Online HD Vectorized Map Construction [8.142552362749655]
  High-definition (HD) maps are crucial to autonomous driving, providing structured representations of road elements to support navigation and planning.<n>We introduce a novel end-to-end framework for consistent online HD vectorized map construction, which jointly performs map instance tracking and short-term prediction.<n>Our proposed method outperforms state-of-the-art (SOTA) methods with good efficiency.
  arXiv  Detail & Related papers  (2026-02-18T18:08:26Z)
• UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network [75.9933952886197]
  UncTrack is a novel uncertainty-aware transformer tracker that predicts the target localization uncertainty.<n>Our method outperforms other state-of-the-art methods in experiments.
  arXiv  Detail & Related papers  (2025-03-17T07:33:16Z)
• HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking [24.21124150354725]
  We propose a novel end-to-end tracking framework global map construction by temporally tracking map elements' historical trajectories.<n>We introduce a Map-Trajectory Prior Fusion module within this tracking framework, leveraging historical priors for tracked instances to improve temporal smoothness and continuity.<n>Substantial experiments on the nuScenes and Argoverse2 datasets demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in both single-frame and temporal metrics.
  arXiv  Detail & Related papers  (2025-03-10T10:44:43Z)
• Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding [8.857237929151795]
  MFTP is a Map-Free Trajectory Prediction method that offers several advantages. First, it eliminates the need for HD maps during inference while still benefiting from map priors during training via knowledge distillation. Second, we present a novel hierarchical encoder that effectively extracts spatial-temporal agent features and aggregates them into multiple trajectory queries.
  arXiv  Detail & Related papers  (2024-11-17T04:50:44Z)
• Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking [52.04679257903805]
  Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T07:48:45Z)
• HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention [76.37139809114274]
  HPNet is a novel dynamic trajectory forecasting method. We propose a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions. Our code is available at https://github.com/XiaolongTang23/HPNet.
  arXiv  Detail & Related papers  (2024-04-09T14:42:31Z)
• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)
• MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping [21.5611219371754]
  The paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking task and uses a history of memory latents to ensure consistent reconstructions over time. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respectively.
  arXiv  Detail & Related papers  (2024-03-23T23:05:25Z)
• MapTrack: Tracking in the Map [14.991113420276767]
  Multi-Object Tracking (MOT) aims to maintain stable and uninterrupted trajectories for each target. Most state-of-the-art approaches first detect objects in each frame and then implement data association between new detections and existing tracks. We propose a new framework comprising of three lightweight and plug-and-play algorithms: the probability map, the prediction map, and the covariance adaptive Kalman filter.
  arXiv  Detail & Related papers  (2024-02-20T12:35:23Z)
• Uncertainty-aware Unsupervised Multi-Object Tracking [33.53331700312752]
  unsupervised multi-object trackers are inferior to learning reliable feature embeddings. Recent self-supervised techniques are adopted, whereas they failed to capture temporal relations. This paper argues that though the uncertainty problem is inevitable, it is possible to leverage the uncertainty itself to improve the learned consistency in turn.
  arXiv  Detail & Related papers  (2023-07-28T09:03:06Z)
• TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses [51.60422927416087]
  3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. We present TrajectoryFormer, a novel point-cloud-based 3D MOT framework.
  arXiv  Detail & Related papers  (2023-06-09T13:31:50Z)
• GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting [121.42898228997538]
  We propose an efficient shared encoding for all agents and the map without sacrificing accuracy or generalization. We leverage pair-wise relative positional encodings to represent geometric relationships between the agents and the map elements in a heterogeneous spatial graph. Our decoder is also viewpoint agnostic, predicting agent goals on the lane graph to enable diverse and context-aware multimodal prediction.
  arXiv  Detail & Related papers  (2022-11-04T16:10:50Z)

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.