Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

• URL: http://arxiv.org/abs/2001.03093v5
• Date: Wed, 13 Jan 2021 18:53:02 GMT
• Title: Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
• Authors: Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, Marco Pavone
• Abstract summary: 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.
• Score: 37.176411554794214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.

Related papers

• Multi-Transmotion: Pre-trained Model for Human Motion Prediction [68.87010221355223]
  Multi-Transmotion is an innovative transformer-based model designed for cross-modality pre-training. Our methodology demonstrates competitive performance across various datasets on several downstream tasks.
  arXiv  Detail & Related papers  (2024-11-04T23:15:21Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Class-Aware Attention for Multimodal Trajectory Prediction [0.7130302992490973]
  We present a novel framework for multimodal trajectory prediction in autonomous driving. Our model is able to run in real-time, achieving a high inference rate of over 300 FPS.
  arXiv  Detail & Related papers  (2022-08-31T18:43:23Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• 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)
• MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control [46.86174832000696]
  Reasoning about human motion is a core component of modern human-robot interactive systems. One of the main uses of behavior prediction in autonomous systems is to inform robot motion planning and control. We propose a new output representation for trajectory forecasting that is more amenable to downstream planning and control use.
  arXiv  Detail & Related papers  (2020-09-16T07:32:37Z)
• Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene [11.91073327154494]
  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.
  arXiv  Detail & Related papers  (2020-05-27T02:32:55Z)
• 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.