Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene

• URL: http://arxiv.org/abs/2005.13133v1
• Date: Wed, 27 May 2020 02:32:55 GMT
• Title: Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene
• Authors: Yanliang Zhu, Dongchun Ren, Mingyu Fan, Deheng Qian, Xin Li, Huaxia Xia
• Abstract summary: We present a novel method for robust trajectory forecasting of multiple agents in dynamic scenes. The proposed method outperforms the state-of-the-art prediction methods in terms of prediction accuracy.
• Score: 11.91073327154494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving. The problem is a great challenge because of the complex interactions among the agents and their interactions with the surrounding scenes. In this paper, we present a novel method for the robust trajectory forecasting of multiple intelligent agents in dynamic scenes. The proposed method consists of three major interrelated components: an interaction net for global spatiotemporal interactive feature extraction, an environment net for decoding dynamic scenes (i.e., the surrounding road topology of an agent), and a prediction net that combines the spatiotemporal feature, the scene feature, the past trajectories of agents and some random noise for the robust trajectory prediction of agents. Experiments on pedestrian-walking and vehicle-pedestrian heterogeneous datasets demonstrate that the proposed method outperforms the state-of-the-art prediction methods in terms of prediction accuracy.

Related papers

• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction [12.84508682310717]
  We propose LatentFormer, a transformer-based model for predicting future vehicle trajectories. We evaluate the proposed method on the nuScenes benchmark dataset and show that our approach achieves state-of-the-art performance and improves upon trajectory metrics by up to 40%.
  arXiv  Detail & Related papers  (2022-03-03T17:44:58Z)
• Unlimited Neighborhood Interaction for Heterogeneous Trajectory Prediction [97.40338982628094]
  We propose a simple yet effective Unlimited Neighborhood Interaction Network (UNIN) which predicts trajectories of heterogeneous agents in multiply categories. Specifically, the proposed unlimited neighborhood interaction module generates the fused-features of all agents involved in an interaction simultaneously. A hierarchical graph attention module is proposed to obtain category-tocategory interaction and agent-to-agent interaction.
  arXiv  Detail & Related papers  (2021-07-31T13:36:04Z)
• 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)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [78.74510891099395]
  In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene.
  arXiv  Detail & Related papers  (2020-07-23T14:31:25Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)
• 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)
• MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic [35.22312783822563]
  Trajectory prediction in urban mixedtraffic zones is critical for many intelligent transportation systems. We propose an approach named Multi-Context Network (MCENET) that is trained by encoding both past and future scene context. In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories.
  arXiv  Detail & Related papers  (2020-02-14T11:04:41Z)
• Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data [37.176411554794214]
  Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. We present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents. We demonstrate its performance on several challenging real-world trajectory forecasting datasets.
  arXiv  Detail & Related papers  (2020-01-09T16:47:17Z)

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.