Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery

• URL: http://arxiv.org/abs/2404.19141v1
• Date: Mon, 29 Apr 2024 22:54:35 GMT
• Title: Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery
• Authors: Tonglong Wei, Youfang Lin, Yan Lin, Shengnan Guo, Lan Zhang, Huaiyu Wan,
• Abstract summary: 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.
• Score: 21.911875343270683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in intelligent transportation systems. Although recent studies have demonstrated the advantages of achieving map-constrained trajectory recovery via an end-to-end manner, they still face two significant challenges. Firstly, existing methods are mostly sequence-based models. It is extremely hard for them to comprehensively capture the micro-semantics of individual trajectory, including the information of each GPS point and the movement between two GPS points. Secondly, existing approaches ignore the impact of the macro-semantics, i.e., the road conditions and the people's shared travel preferences reflected by a group of trajectories. To address the above challenges, we propose a Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder (MM-STGED). Specifically, we model each trajectory as a graph to efficiently describe the micro-semantics of trajectory and design a novel message-passing mechanism to learn trajectory representations. Additionally, we extract the macro-semantics of trajectories and further incorporate them into a well-designed graph-based decoder to guide trajectory recovery. Extensive experiments conducted on sparse trajectories with three different sampling intervals that are respectively constructed from two real-world trajectory datasets demonstrate the superiority of our proposed model.

Related papers

• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning [26.630640299709114]
  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.
  arXiv  Detail & Related papers  (2024-02-25T18:27:25Z)
• 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)
• A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery [27.347708962204713]
  This work is dedicated to offering a more robust trajectory recovery for complex trajectories. We propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training. The results prove that our method learns better representations for trajectory recovery, with 5.22% higher F1-score overall and 8.16% higher F1-score for complex trajectories particularly.
  arXiv  Detail & Related papers  (2023-11-05T12:20:39Z)
• Layout Sequence Prediction From Noisy Mobile Modality [53.49649231056857]
  Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. We propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories.
  arXiv  Detail & Related papers  (2023-10-09T20:32:49Z)
• An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds [50.19288542498838]
  3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. We introduce a motion-centric paradigm to handle LiDAR SOT from a new perspective.
  arXiv  Detail & Related papers  (2023-03-21T17:28:44Z)
• RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer [15.350300338463969]
  We propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It then introduces a Sub-Graph Generation module to represent each GPS point as a sub-graph structure of the road network around the GPS point.
  arXiv  Detail & Related papers  (2022-11-23T11:28:32Z)
• 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)
• Trajectory Prediction with Graph-based Dual-scale Context Fusion [43.51107329748957]
  We present a graph-based trajectory prediction network named the Dual Scale Predictor. It encodes both the static and dynamical driving context in a hierarchical manner. Thanks to the proposed dual-scale context fusion network, our DSP is able to generate accurate and human-like multi-modal trajectories.
  arXiv  Detail & Related papers  (2021-11-02T13:42:16Z)
• Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation [62.79160608266713]
  morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping. Existing grasp generation approaches are cursed to construct discontinuous grasp maps by aggregating annotations for drastically different orientations per grasping point. We propose a novel augmented grasp map representation, suitable for pixel-wise synthesis, that locally disentangles grasping orientations by partitioning the angle space into multiple bins.
  arXiv  Detail & Related papers  (2020-06-09T08:54:54Z)

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.