Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights

• URL: http://arxiv.org/abs/2501.07711v1
• Date: Mon, 13 Jan 2025 21:45:01 GMT
• Title: Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights
• Authors: Jiajia Xie, Sheng Zhang, Beihao Xia, Zhu Xiao, Hongbo Jiang, Siwang Zhou, Zheng Qin, Hongyang Chen,
• Abstract summary: Existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. We propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data. Our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.
• Score: 23.95745936419479
• License:
• Abstract: Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7\% and 39.3\% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.

Related papers

• Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information [17.09138102827048]
  Accurately predicting pedestrian trajectories requires a deep understanding of individual behaviors, social interactions, and road environments. We propose a novel approach incorporating trip information as a new modality into pedestrian trajectory models. By exploring crowd behavior within the road network, our approach shows great promise in improving pedestrian safety through accurate trajectory predictions.
  arXiv  Detail & Related papers  (2024-09-23T17:11:31Z)
• Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction [15.454206825258169]
  Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians. This paper proposes a graph transformer structure to improve prediction performance.
  arXiv  Detail & Related papers  (2024-01-10T01:50:29Z)
• SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction [59.064925464991056]
  We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-05T01:49:18Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking [23.608125748229174]
  We propose a generic generative neural system for multi-agent trajectory prediction involving heterogeneous agents. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2021-02-18T02:25:35Z)
• 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)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network [29.289670231364788]
  In this paper, we propose a generic generative neural system for multi-agent trajectory prediction. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2020-02-14T20:11:13Z)

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.