Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting

• URL: http://arxiv.org/abs/2407.09475v2
• Date: Fri, 20 Dec 2024 05:34:30 GMT
• Title: Adaptive Prediction Ensemble: Improving Out-of-Distribution Generalization of Motion Forecasting
• Authors: Jinning Li, Jiachen Li, Sangjae Bae, David Isele,
• Abstract summary: We propose a novel framework, Adaptive Prediction Ensemble (APE), which integrates deep learning and rule-based prediction experts. A learned routing function, trained concurrently with the deep learning model, dynamically selects the most reliable prediction based on the input scenario. We show that our method outperforms individual prediction models and other variants, particularly in long-horizon prediction and scenarios with a high proportion of OOD data.
• Score: 15.916325272109454
• License:
• Abstract: Deep learning-based trajectory prediction models for autonomous driving often struggle with generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than simple rule-based models. To address this limitation, we propose a novel framework, Adaptive Prediction Ensemble (APE), which integrates deep learning and rule-based prediction experts. A learned routing function, trained concurrently with the deep learning model, dynamically selects the most reliable prediction based on the input scenario. Our experiments on large-scale datasets, including Waymo Open Motion Dataset (WOMD) and Argoverse, demonstrate improvement in zero-shot generalization across datasets. We show that our method outperforms individual prediction models and other variants, particularly in long-horizon prediction and scenarios with a high proportion of OOD data. This work highlights the potential of hybrid approaches for robust and generalizable motion prediction in autonomous driving. More details can be found on the project page: https://sites.google.com/view/ape-generalization.

Related papers

• Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach [54.429396802848224]
  This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
  arXiv  Detail & Related papers  (2024-03-10T04:16:04Z)
• AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction [6.724750970258851]
  We propose a modular model-agnostic framework for trajectory prediction. Each expert is trained with a specialized skill with respect to a particular part of the data. To produce predictions, we utilise a router network that selects the best expert by generating relative confidence scores.
  arXiv  Detail & Related papers  (2024-02-13T02:43:41Z)
• Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction [15.454206825258169]
  Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians. This paper proposes a graph transformer structure to improve prediction performance.
  arXiv  Detail & Related papers  (2024-01-10T01:50:29Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• LOPR: Latent Occupancy PRediction using Generative Models [49.15687400958916]
  LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation. We propose a framework that decouples occupancy prediction into: representation learning and prediction within the learned latent space.
  arXiv  Detail & Related papers  (2022-10-03T22:04:00Z)
• HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling [27.194900145235007]
  We introduce HYPER, a general and expressive hybrid prediction framework. By modeling traffic agents as a hybrid discrete-continuous system, our approach is capable of predicting discrete intent changes over time. We train and validate our model on the Argoverse dataset, and demonstrate its effectiveness through comprehensive ablation studies and comparisons with state-of-the-art models.
  arXiv  Detail & Related papers  (2021-10-05T20:20:10Z)
• Injecting Knowledge in Data-driven Vehicle Trajectory Predictors [82.91398970736391]
  Vehicle trajectory prediction tasks have been commonly tackled from two perspectives: knowledge-driven or data-driven. In this paper, we propose to learn a "Realistic Residual Block" (RRB) which effectively connects these two perspectives. Our proposed method outputs realistic predictions by confining the residual range and taking into account its uncertainty.
  arXiv  Detail & Related papers  (2021-03-08T16:03:09Z)
• 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)
• Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning [137.39196753245105]
  We present a new model-based reinforcement learning algorithm that learns a multi-headed dynamics model for dynamics generalization. We incorporate context learning, which encodes dynamics-specific information from past experiences into the context latent vector. Our method exhibits superior zero-shot generalization performance across a variety of control tasks, compared to state-of-the-art RL methods.
  arXiv  Detail & Related papers  (2020-10-26T03:20:42Z)

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.