Aware of the History: Trajectory Forecasting with the Local Behavior Data

• URL: http://arxiv.org/abs/2207.09646v1
• Date: Wed, 20 Jul 2022 04:35:38 GMT
• Title: Aware of the History: Trajectory Forecasting with the Local Behavior Data
• Authors: Yiqi Zhong, Zhenyang Ni, Siheng Chen, Ulrich Neumann
• Abstract summary: Local behavior data is a new type of input data for trajectory forecasting systems. We propose a novel local-behavior-aware (LBA) prediction framework that improves forecasting accuracy. We also employ a local-behavior-free (LBF) prediction framework, which adopts a knowledge-distillation-based architecture to infer the impact of missing data.
• Score: 30.90992947135638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The historical trajectories previously passing through a location may help infer the future trajectory of an agent currently at this location. Despite great improvements in trajectory forecasting with the guidance of high-definition maps, only a few works have explored such local historical information. In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories. Local behavior data helps the systems emphasize the prediction locality and better understand the impact of static map objects on moving agents. We propose a novel local-behavior-aware (LBA) prediction framework that improves forecasting accuracy by fusing information from observed trajectories, HD maps, and local behavior data. Also, where such historical data is insufficient or unavailable, we employ a local-behavior-free (LBF) prediction framework, which adopts a knowledge-distillation-based architecture to infer the impact of missing data. Extensive experiments demonstrate that upgrading existing methods with these two frameworks significantly improves their performances. Especially, the LBA framework boosts the SOTA methods' performance on the nuScenes dataset by at least 14% for the K=1 metrics.

Related papers

• Beyond Features: How Dataset Design Influences Multi-Agent Trajectory Prediction Performance [37.850085364753845]
  This work examines how feature selection, cross-dataset transfer, and geographic diversity influence trajectory prediction accuracy in multi-agent settings.<n>We evaluate a state-of-the-art model using our novel L4 Motion Forecasting dataset based on our own data recordings in Germany and the US.
  arXiv  Detail & Related papers  (2025-07-07T15:18:51Z)
• Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction [37.930452438916795]
  We propose a plug-and-play framework for long-tailed mobility prediction in an exploitation and exploration manner.<n>First, we construct city-tailored location hierarchy based on Large Language Models (LLMs) by exploiting Maslow's theory of human motivation.<n>Experiments on state-of-the-art models across six datasets demonstrate the framework's consistent effectiveness and generalizability.
  arXiv  Detail & Related papers  (2025-05-26T13:26:35Z)
• Enhancing Training Data Attribution with Representational Optimization [57.61977909113113]
  Training data attribution methods aim to measure how training data impacts a model's predictions.<n>We propose AirRep, a representation-based approach that closes this gap by learning task-specific and model-aligned representations explicitly for TDA.<n>AirRep introduces two key innovations: a trainable encoder tuned for attribution quality, and an attention-based pooling mechanism that enables accurate estimation of group-wise influence.
  arXiv  Detail & Related papers  (2025-05-24T05:17:53Z)
• Unified Human Localization and Trajectory Prediction with Monocular Vision [64.19384064365431]
  MonoTransmotion is a Transformer-based framework that uses only a monocular camera to jointly solve localization and prediction tasks. We show that by jointly training both tasks with our unified framework, our method is more robust in real-world scenarios made of noisy inputs.
  arXiv  Detail & Related papers  (2025-03-05T14:18:39Z)
• Capturing the Temporal Dependence of Training Data Influence [100.91355498124527]
  We formalize the concept of trajectory-specific leave-one-out influence, which quantifies the impact of removing a data point during training. We propose data value embedding, a novel technique enabling efficient approximation of trajectory-specific LOO. As data value embedding captures training data ordering, it offers valuable insights into model training dynamics.
  arXiv  Detail & Related papers  (2024-12-12T18:28:55Z)
• RealTraj: Towards Real-World Pedestrian Trajectory Forecasting [10.332817296500533]
  We propose a novel framework, RealTraj, that enhances the real-world applicability of trajectory forecasting. We present Det2TrajFormer, a model that remains invariant to tracking noise by using past detections as inputs. Unlike previous trajectory forecasting methods, our approach fine-tunes the model using only ground-truth detections, reducing the need for costly person ID annotations.
  arXiv  Detail & Related papers  (2024-11-26T12:35:26Z)
• 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)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It [2.1665407462280446]
  State-of-the-art methods are trained predominantly using nuScenes and Argoverse 2 datasets. Over $80$% of nuScenes and $40$% of Argoverse 2 validation and test samples are less than $5$ m from a training sample. We propose geographically disjoint data splits to reveal the true performance in unseen environments.
  arXiv  Detail & Related papers  (2023-12-11T14:43:23Z)
• PEANUT: Predicting and Navigating to Unseen Targets [18.87376347895365]
  Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. We present a method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data.
  arXiv  Detail & Related papers  (2022-12-05T18:58:58Z)
• On the Effective Usage of Priors in RSS-based Localization [56.68864078417909]
  We propose a Received Signal Strength (RSS) fingerprint and convolutional neural network-based algorithm, LocUNet. In this paper, we study the localization problem in dense urban settings. We first recognize LocUNet's ability to learn the underlying prior distribution of the Rx position or Rx and transmitter (Tx) association preferences from the training data, and attribute its high performance to these.
  arXiv  Detail & Related papers  (2022-11-28T00:31:02Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• Trajectory Test-Train Overlap in Next-Location Prediction Datasets [5.039138978031649]
  Next-location prediction has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. This paper tests the generalization capability of these predictors on public mobility datasets. We propose a methodology to rerank the outputs of the next-location predictors based on spatial mobility patterns.
  arXiv  Detail & Related papers  (2022-03-07T08:39:45Z)
• Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis [96.53859361560505]
  Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. There always exists severe domain shift between the pretraining and downstream ABSA datasets. We introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline.
  arXiv  Detail & Related papers  (2021-10-26T04:03:45Z)
• Injecting Knowledge in Data-driven Vehicle Trajectory Predictors [82.91398970736391]
  Vehicle trajectory prediction tasks have been commonly tackled from two perspectives: knowledge-driven or data-driven. In this paper, we propose to learn a "Realistic Residual Block" (RRB) which effectively connects these two perspectives. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty.
  arXiv  Detail & Related papers  (2021-03-08T16:03:09Z)
• FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing [12.518673970373422]
  Data-driven learning model-based cooperative localization and location data processing are considered. We first review state-of-the-art algorithms in the context of federated learning. We demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works.
  arXiv  Detail & Related papers  (2020-03-08T01:51:56Z)

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.