On complementing end-to-end human motion predictors with planning

• URL: http://arxiv.org/abs/2103.05661v1
• Date: Tue, 9 Mar 2021 19:02:45 GMT
• Title: On complementing end-to-end human motion predictors with planning
• Authors: Liting Sun, Xiaogang Jia, Anca D. Dragan
• Abstract summary: High capacity end-to-end approaches for human motion prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events. Planning-based prediction, on the other hand, can reliably output decent-but-not-great predictions.
• Score: 31.025766804649464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High capacity end-to-end approaches for human motion prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events. Planning-based prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift, but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal. In this work, we analyze one family of approaches that strive to get the best of both worlds: use the end-to-end predictor on common cases, but do not rely on it for tail events / out-of-distribution inputs -- switch to the planning-based predictor there. We contribute an analysis of different approaches for detecting when to make this switch, using an autonomous driving domain. We find that promising approaches based on ensembling or generative modeling of the training distribution might not be reliable, but that there very simple methods which can perform surprisingly well -- including training a classifier to pick up on tell-tale issues in predicted trajectories.

Related papers

• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• 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)
• Human Trajectory Forecasting with Explainable Behavioral Uncertainty [63.62824628085961]
  Human trajectory forecasting helps to understand and predict human behaviors, enabling applications from social robots to self-driving cars. Model-free methods offer superior prediction accuracy but lack explainability, while model-based methods provide explainability but cannot predict well. We show that BNSP-SFM achieves up to a 50% improvement in prediction accuracy, compared with 11 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-04T16:45:21Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• 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)
• Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making [1.384055225262046]
  We focus on predictions expressed as General Value Functions: temporally extended estimates of the accumulation of a future signal. One challenge is determining from the infinitely many predictions that the agent could possibly make which might support decision-making. By learning, rather than manually specifying these predictions, we enable the agent to identify useful predictions in a self-supervised manner.
  arXiv  Detail & Related papers  (2021-11-18T20:17:07Z)
• Propagating State Uncertainty Through Trajectory Forecasting [34.53847097769489]
  Trajectory forecasting is surrounded by uncertainty as its inputs are produced by (noisy) upstream perception. Most trajectory forecasting methods do not account for upstream uncertainty, instead taking only the most-likely values. We present a novel method for incorporating perceptual state uncertainty in trajectory forecasting, a key component of which is a new statistical distance-based loss function.
  arXiv  Detail & Related papers  (2021-10-07T08:51:16Z)
• 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)
• Probabilistic Human Motion Prediction via A Bayesian Neural Network [71.16277790708529]
  We propose a probabilistic model for human motion prediction in this paper. Our model could generate several future motions when given an observed motion sequence. We extensively validate our approach on a large scale benchmark dataset Human3.6m.
  arXiv  Detail & Related papers  (2021-07-14T09:05:33Z)
• Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation [25.97333838935589]
  Uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. Existing algorithms can overestimate certainty, possibly yielding a false sense of confidence in the predictive model.
  arXiv  Detail & Related papers  (2020-02-29T20:31:04Z)

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.