Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles

• URL: http://arxiv.org/abs/2501.00890v1
• Date: Wed, 01 Jan 2025 16:37:24 GMT
• Title: Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles
• Authors: Ouhan Huang, Huanle Rao, Xiaowen Cai, Tianyun Wang, Aolong Sun, Sizhe Xing, Yifan Sun, Gangyong Jia,
• Abstract summary: This study introduces the Spatio Temporal Attention-based methodology for Vehicle Trajectory Prediction Target (VTPred) We map the vehicle trajectory onto a graph directed, after which spatial attributes are extracted via a Graph Attention Networks(GATs) The Transformer technology is employed to yield temporal features from the sequence, resulting in precise trajectory prediction.
• Score: 4.1268583353286
• License:
• Abstract: Forecasting vehicle behavior within complex traffic environments is pivotal within Intelligent Transportation Systems (ITS). Though this technology plays a significant role in alleviating the prevalent operational difficulties in logistics and transportation systems, the precise prediction of vehicle trajectories still poses a substantial challenge. To address this, our study introduces the Spatio Temporal Attention-based methodology for Target Vehicle Trajectory Prediction (STATVTPred). This approach integrates Global Positioning System(GPS) localization technology to track target movement and dynamically predict the vehicle's future path using comprehensive spatio-temporal trajectory data. We map the vehicle trajectory onto a directed graph, after which spatial attributes are extracted via a Graph Attention Networks(GATs). The Transformer technology is employed to yield temporal features from the sequence. These elements are then amalgamated with local road network structure maps to filter and deliver a smooth trajectory sequence, resulting in precise vehicle trajectory prediction.This study validates our proposed STATVTPred method on T-Drive and Chengdu taxi-trajectory datasets. The experimental results demonstrate that STATVTPred achieves 6.38% and 10.55% higher Average Match Rate (AMR) than the Transformer model on the Beijing and Chengdu datasets, respectively. Compared to the LSTM Encoder-Decoder model, STATVTPred boosts AMR by 37.45% and 36.06% on the same datasets. This is expected to establish STATVTPred as a new approach for handling trajectory prediction of targets in logistics and transportation scenarios, thereby enhancing prediction accuracy.

Related papers

• A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses [68.68514648185828]
  Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles. Current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements. This study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss.
  arXiv  Detail & Related papers  (2024-11-29T14:47:08Z)
• MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles [8.229161517598373]
  We leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths.
  arXiv  Detail & Related papers  (2024-07-08T10:45:30Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety [3.3659635625913564]
  We have developed a dataset for short-term trajectory prediction tasks using the CARLA simulator. This dataset is extensive and incorporates what is considered complex scenarios - pedestrians crossing the road, vehicles overtaking. An end-to-end short-term trajectory prediction model using convolutional neural networks (CNN) and long short-term memory (LSTM) networks has also been developed.
  arXiv  Detail & Related papers  (2023-07-11T14:28:33Z)
• An End-to-End Vehicle Trajcetory Prediction Framework [3.7311680121118345]
  An accurate prediction of a future trajectory does not just rely on the previous trajectory, but also a simulation of the complex interactions between other vehicles nearby. Most state-of-the-art networks built to tackle the problem assume readily available past trajectory points. We propose a novel end-to-end architecture that takes raw video inputs and outputs future trajectory predictions.
  arXiv  Detail & Related papers  (2023-04-19T15:42:03Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving [2.6774008509841005]
  This paper proposes a graph-based spatial-temporal convolutional network ( GSTCN) to predict future trajectory distributions of all neighbor vehicles. The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM)
  arXiv  Detail & Related papers  (2021-09-27T02:20:38Z)
• Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism [57.68557165836806]
  We propose a model to predict the trajectories of target agents around an autonomous vehicle. We encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease.
  arXiv  Detail & Related papers  (2020-07-06T03:48:08Z)
• PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [82.97006521937101]
  We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. We propose Net, an end-to-end model that takes as input sensor data, and outputs at each time step object tracks and their future level.
  arXiv  Detail & Related papers  (2020-05-29T17:57:25Z)
• TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
  Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework. TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals. Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-04-26T00:01:49Z)
• GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction [6.12727713172576]
  Many AI-oriented companies, such as Google, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles.
  arXiv  Detail & Related papers  (2020-03-22T03:24:31Z)

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.