ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation

• URL: http://arxiv.org/abs/2307.14187v1
• Date: Wed, 26 Jul 2023 13:41:51 GMT
• Title: ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation
• Authors: G\"orkay Aydemir, Adil Kaan Akan, Fatma G\"uney
• Abstract summary: ADAPT is a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning. Our approach outperforms state-of-the-art methods in both single-agent and multi-agent settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting future trajectories of agents in complex traffic scenes requires reliable and efficient predictions for all agents in the scene. However, existing methods for trajectory prediction are either inefficient or sacrifice accuracy. To address this challenge, we propose ADAPT, a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning. Our approach outperforms state-of-the-art methods in both single-agent and multi-agent settings on the Argoverse and Interaction datasets, with a fraction of their computational overhead. We attribute the improvement in our performance: first, to the adaptive head augmenting the model capacity without increasing the model size; second, to our design choices in the endpoint-conditioned prediction, reinforced by gradient stopping. Our analyses show that ADAPT can focus on each agent with adaptive prediction, allowing for accurate predictions efficiently. https://KUIS-AI.github.io/adapt

Related papers

• Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models [68.23649978697027]
  Forecast-PEFT is a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks. Forecast-FT further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods.
  arXiv  Detail & Related papers  (2024-07-28T19:18:59Z)
• Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network [1.5888246742280365]
  Trajectory prediction is crucial for autonomous driving as it aims to forecast future movements of traffic participants. Traditional methods usually perform holistic inference on trajectories of agents, neglecting the differences in difficulty among agents. This paper proposes a novel DifficultyGuided Feature Enhancement (DGFNet), which leverages the prediction difficulty differences among agents.
  arXiv  Detail & Related papers  (2024-07-26T07:04:30Z)
• Valeo4Cast: A Modular Approach to End-to-End Forecasting [93.86257326005726]
  Our solution ranks first in the Argoverse 2 End-to-end Forecasting Challenge, with 63.82 mAPf. We depart from the current trend of tackling this task via end-to-end training from perception to forecasting, and instead use a modular approach. We surpass forecasting results by +17.1 points over last year's winner and by +13.3 points over this year's runner-up.
  arXiv  Detail & Related papers  (2024-06-12T11:50:51Z)
• MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction [6.110153599741102]
  There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Existing prediction models yield joint predictions of agents' future trajectories with uncertainty weights or marginal Gaussian probability density functions (PDFs) for single agents. This paper introduces a novel approach to motion prediction, focusing on predicting agent-pair covariance matrices in a scene-centric'' manner.
  arXiv  Detail & Related papers  (2024-04-30T06:21:42Z)
• Certified Human Trajectory Prediction [66.1736456453465]
  Tray prediction plays an essential role in autonomous vehicles. We propose a certification approach tailored for the task of trajectory prediction. We address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality.
  arXiv  Detail & Related papers  (2024-03-20T17:41:35Z)
• SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction [37.461695201579914]
  We introduce a novel scenario-adaptive refinement strategy, named SmartRefine, to refine prediction with minimal additional computation. SmartRefine is designed as a generic and flexible approach that can be seamlessly integrated into most state-of-the-art motion prediction models. By adding SmartRefine to QCNet, we outperform all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at submission.
  arXiv  Detail & Related papers  (2024-03-18T05:53:20Z)
• QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction [5.312631388611489]
  Estimating the joint distribution of on-road agents' future trajectories is essential for autonomous driving. We propose a next-generation framework for joint multi-agent trajectory prediction called QCNeXt. Our approach ranks 1st on the Argoverse 2 multi-agent motion forecasting benchmark.
  arXiv  Detail & Related papers  (2023-06-18T09:40:40Z)
• GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting [121.42898228997538]
  We propose an efficient shared encoding for all agents and the map without sacrificing accuracy or generalization. We leverage pair-wise relative positional encodings to represent geometric relationships between the agents and the map elements in a heterogeneous spatial graph. Our decoder is also viewpoint agnostic, predicting agent goals on the lane graph to enable diverse and context-aware multimodal prediction.
  arXiv  Detail & Related papers  (2022-11-04T16:10:50Z)
• Transforming Model Prediction for Tracking [109.08417327309937]
  Transformers capture global relations with little inductive bias, allowing it to learn the prediction of more powerful target models. We train the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets. Our tracker sets a new state of the art on three benchmarks, achieving an AUC of 68.5% on the challenging LaSOT dataset.
  arXiv  Detail & Related papers  (2022-03-21T17:59:40Z)
• 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)
• 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)

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.