Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

• URL: http://arxiv.org/abs/2210.15449v1
• Date: Wed, 19 Oct 2022 15:14:46 GMT
• Title: Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
• Authors: Ding Li and Qichao Zhang and Shuai Lu and Yifeng Pan and Dongbin Zhao
• Abstract summary: Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. New goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents.
• Score: 10.190939530193502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods.

Related papers

• A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction [6.137256382926171]
  This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet) The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets.
  arXiv  Detail & Related papers  (2024-06-26T03:59:21Z)
• SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction [4.286256266868156]
  We present SSL-Interactions that proposes pretext tasks to enhance interaction modeling for trajectory prediction. We introduce four interaction-aware pretext tasks to encapsulate various aspects of agent interactions. We also propose an approach to curate interaction-heavy scenarios from datasets.
  arXiv  Detail & Related papers  (2024-01-15T14:43:40Z)
• Interpretable Long Term Waypoint-Based Trajectory Prediction Model [1.4778851751964937]
  We study the impact of adding a long-term goal on the performance of a trajectory prediction framework. We present an interpretable long term waypoint-driven prediction framework (WayDCM)
  arXiv  Detail & Related papers  (2023-12-11T09:10:22Z)
• FFINet: Future Feedback Interaction Network for Motion Forecasting [46.247396728154904]
  We propose a novel Future Feedback Interaction Network (FFINet) to aggregate features the current observations and potential future interactions for trajectory prediction. Our FFINet achieves the state-of-the-art performance on Argoverse 1 and Argoverse 2 motion forecasting benchmarks.
  arXiv  Detail & Related papers  (2023-11-08T07:57:29Z)
• Parallel Reasoning Network for Human-Object Interaction Detection [53.422076419484945]
  We propose a new transformer-based method named Parallel Reasoning Network(PR-Net) PR-Net constructs two independent predictors for instance-level localization and relation-level understanding. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
  arXiv  Detail & Related papers  (2023-01-09T17:00:34Z)
• FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs [8.63314005149641]
  We propose FJMP, a Factorized Joint Motion Prediction framework for interactive driving scenarios. FJMP produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION dataset.
  arXiv  Detail & Related papers  (2022-11-27T18:59:17Z)
• ProspectNet: Weighted Conditional Attention for Future Interaction Modeling in Behavior Prediction [5.520507323174275]
  We formulate the end-to-end joint prediction problem as a sequential learning process of marginal learning and joint learning of vehicle behaviors. We propose ProspectNet, a joint learning block that adopts the weighted attention score to model the mutual influence between interactive agent pairs. We show that ProspectNet outperforms the Cartesian product of two marginal predictions, and achieves comparable performance on the Interactive Motion Prediction benchmarks.
  arXiv  Detail & Related papers  (2022-08-29T19:29:49Z)
• 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)
• Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network [65.11999700562869]
  We propose a novel collaborative prediction unit (CoPU), which aggregates predictions from multiple collaborative predictors according to a collaborative graph. Our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average.
  arXiv  Detail & Related papers  (2021-07-02T08:20:06Z)
• End-to-end Contextual Perception and Prediction with Interaction Transformer [79.14001602890417]
  We tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer architecture. Our model can be trained end-to-end, and runs in real-time.
  arXiv  Detail & Related papers  (2020-08-13T14:30:12Z)
• 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)

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.