More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning

• URL: http://arxiv.org/abs/2402.16915v1
• Date: Sun, 25 Feb 2024 18:27:25 GMT
• Title: More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning
• Authors: Zhipeng Ma, Zheyan Tu, Xinhai Chen, Yan Zhang, Deguo Xia, Guyue Zhou, Yilun Chen, Yu Zheng, Jiangtao Gong
• Abstract summary: We propose Joint GPS and Route Modelling based on self-supervised technology, namely JGRM. We develop two encoders, each tailored to capture representations of route and GPS trajectories respectively. The representations from the two modalities are fed into a shared transformer for inter-modal information interaction.
• Score: 26.630640299709114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory representation learning plays a pivotal role in supporting various downstream tasks. Traditional methods in order to filter the noise in GPS trajectories tend to focus on routing-based methods used to simplify the trajectories. However, this approach ignores the motion details contained in the GPS data, limiting the representation capability of trajectory representation learning. To fill this gap, we propose a novel representation learning framework that Joint GPS and Route Modelling based on self-supervised technology, namely JGRM. We consider GPS trajectory and route as the two modes of a single movement observation and fuse information through inter-modal information interaction. Specifically, we develop two encoders, each tailored to capture representations of route and GPS trajectories respectively. The representations from the two modalities are fed into a shared transformer for inter-modal information interaction. Eventually, we design three self-supervised tasks to train the model. We validate the effectiveness of the proposed method on two real datasets based on extensive experiments. The experimental results demonstrate that JGRM outperforms existing methods in both road segment representation and trajectory representation tasks. Our source code is available at Anonymous Github.

Related papers

• Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models [27.316692263196277]
  MVTraj is a novel multi-view modeling method for trajectory representation learning. It integrates diverse contextual knowledge, from GPS to road network and points-of-interest to provide a more comprehensive understanding of trajectory data. Extensive experiments on real-world datasets demonstrate that MVTraj significantly outperforms existing baselines in tasks associated with various spatial views.
  arXiv  Detail & Related papers  (2024-10-17T03:56:12Z)
• Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery [21.911875343270683]
  Recovering missing GPS points in a sparse trajectory could offer deep insights into users' moving behaviors in intelligent transportation systems. It is extremely hard for them to comprehensively capture the micro-semantics of individual trajectory. We propose a Micro-Macro- Spatial Graph-Decoder (MM-STGED) to efficiently describe the micro-semantics of trajectory and design a novel message-passing mechanism.
  arXiv  Detail & Related papers  (2024-04-29T22:54:35Z)
• G-MEMP: Gaze-Enhanced Multimodal Ego-Motion Prediction in Driving [71.9040410238973]
  We focus on inferring the ego trajectory of a driver's vehicle using their gaze data. Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network that combines GPS and video input with gaze data. The results show that G-MEMP significantly outperforms state-of-the-art methods in both benchmarks.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation [13.321587117066166]
  We propose a novel multimodal fusion model, DouFu, for trajectory representation joint learning. We first design movement, route, and global features generated from the trajectory data and urban functional zones. With the global semantic feature, DouFu produces a comprehensive embedding for each trajectory.
  arXiv  Detail & Related papers  (2022-05-05T07:43:35Z)
• Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction [110.61383502442598]
  We introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet) CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. Experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction.
  arXiv  Detail & Related papers  (2021-11-30T04:30:10Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)
• A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN [59.57221522897815]
  We propose a neural network model based on trajectories information for driving behavior recognition. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
  arXiv  Detail & Related papers  (2021-03-01T06:47:29Z)
• Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking [85.333260415532]
  We develop a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities. When the appearance cue is unreliable, we take motion cues into account to make the tracker robust. Numerous results on three recent RGB-T tracking datasets show that the proposed tracker performs significantly better than other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-07-04T08:11:33Z)
• Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization [12.652210024012374]
  One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors. Methods based on top-down sceneization on one side and Generative Adrial Networks (GANs) on the other have shown to be particularly successful. In this paper we build upon these two directions and propose aversa-based conditional GAN architecture. We evaluate the proposed method on a large-scale, real-world data set, showing that it outperforms state-of-the-art GAN-based baselines.
  arXiv  Detail & Related papers  (2020-04-14T00:41:17Z)

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.