Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints

• URL: http://arxiv.org/abs/2309.17338v2
• Date: Sun, 26 Nov 2023 18:53:50 GMT
• Title: Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints
• Authors: Pranav Singh Chib, Pravendra Singh
• Abstract summary: Motion prediction systems must learn spatial and temporal information from the past to forecast the future trajectories of the agent. We propose Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal dependencies during the training of a trajectory prediction model. We evaluate our proposed approach on three datasets: NBA Sports VU, ETH-UCY, and TrajNet++.
• Score: 9.385936248154987
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The inherently diverse and uncertain nature of trajectories presents a formidable challenge in accurately modeling them. Motion prediction systems must effectively learn spatial and temporal information from the past to forecast the future trajectories of the agent. Many existing methods learn temporal motion via separate components within stacked models to capture temporal features. Furthermore, prediction methods often operate under the assumption that observed trajectory waypoint sequences are complete, disregarding scenarios where missing values may occur, which can influence their performance. Moreover, these models may be biased toward particular waypoint sequences when making predictions. We propose a novel approach called Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal dependencies during the training of a trajectory prediction model. By stochastically dropping waypoints from past observed trajectories, the model is forced to learn the underlying temporal representation from the remaining waypoints, resulting in an improved model. Incorporating stochastic temporal waypoint dropping into the model learning process significantly enhances its performance in scenarios with missing values. Experimental results demonstrate our approach's substantial improvement in trajectory prediction capabilities. Our approach can complement existing trajectory prediction methods to improve their prediction accuracy. We evaluate our proposed approach on three datasets: NBA Sports VU, ETH-UCY, and TrajNet++.

Related papers

• Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace [9.688760969026305]
  We propose a data-driven learning framework, that leverages the predictive and feature extraction capabilities of the mixture models and seq2seq-based neural networks. After training with this framework, the learned model can improve long-step prediction accuracy significantly. The accuracy and effectiveness of the approach are evaluated by comparing the predicted trajectories with the ground truth.
  arXiv  Detail & Related papers  (2024-09-25T21:08:25Z)
• Motion Forecasting via Model-Based Risk Minimization [8.766024024417316]
  We propose a novel sampling method applicable to trajectory prediction based on the predictions of multiple models. We first show that conventional sampling based on predicted probabilities can degrade performance due to missing alignment between models. By using state-of-the-art models as base learners, our approach constructs diverse and effective ensembles for optimal trajectory sampling.
  arXiv  Detail & Related papers  (2024-09-16T09:03:28Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention [76.37139809114274]
  HPNet is a novel dynamic trajectory forecasting method. We propose a Historical Prediction Attention module to automatically encode the dynamic relationship between successive predictions. Our code is available at https://github.com/XiaolongTang23/HPNet.
  arXiv  Detail & Related papers  (2024-04-09T14:42:31Z)
• Enhancing Trajectory Prediction through Self-Supervised Waypoint Noise Prediction [9.385936248154987]
  Trajectory prediction is an important task that involves modeling the indeterminate nature of traffic actors to forecast future trajectories. We propose a novel approach called SSWNP (Self-Supervised Waypoint Noise Prediction) In our approach, we first create clean and noise-augmented views of past observed trajectories across the spatial domain of waypoints.
  arXiv  Detail & Related papers  (2023-11-26T19:03:41Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion [88.45326906116165]
  We present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID) We encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method.
  arXiv  Detail & Related papers  (2022-03-25T16:59:08Z)
• 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)
• Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning [7.194382512848327]
  We propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons. Our results in simulated and experimental robotic tasks show that our trajectory-based models yield significantly more accurate long term predictions.
  arXiv  Detail & Related papers  (2020-12-16T18:47:37Z)
• Temporally-Continuous Probabilistic Prediction using Polynomial Trajectory Parameterization [12.896275507449936]
  A commonly-used representation for motion prediction of actors is a sequence of waypoints for each actor at discrete future time-points. This approach is simple and flexible, but it can exhibit unrealistic higher-order derivatives and approximation errors at intermediate time steps. We propose a simple and general representation for temporally continuous trajectory prediction that is based on trajectory parameterization.
  arXiv  Detail & Related papers  (2020-11-01T01:51:44Z)

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.