Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models

• URL: http://arxiv.org/abs/2007.06781v2
• Date: Sat, 10 Oct 2020 01:51:47 GMT
• Title: Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models
• Authors: Nick Lamm, Shashank Jaiprakash, Malavika Srikanth, Iddo Drori
• Abstract summary: We show that semi-supervised models for vehicle trajectory prediction significantly improve performance over supervised models on real-world benchmarks. We perform ablation studies comparing transfer learning of semi-supervised and supervised models while keeping all other factors equal.
• Score: 1.5293427903448022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we show that semi-supervised models for vehicle trajectory prediction significantly improve performance over supervised models on state-of-the-art real-world benchmarks. Moving from supervised to semi-supervised models allows scaling-up by using unlabeled data, increasing the number of images in pre-training from Millions to a Billion. We perform ablation studies comparing transfer learning of semi-supervised and supervised models while keeping all other factors equal. Within semi-supervised models we compare contrastive learning with teacher-student methods as well as networks predicting a small number of trajectories with networks predicting probabilities over a large trajectory set. Our results using both low-level and mid-level representations of the driving environment demonstrate the applicability of semi-supervised methods for real-world vehicle trajectory prediction.

Related papers

• Target-driven Self-Distillation for Partial Observed Trajectories Forecasting [41.636125879090116]
  We introduce a Target-driven Self-Distillation method (TSD) for motion forecasting. By employing self-distillation, the model learns from the feature distributions of both fully observed and partially observed trajectories. This enhances the model's ability to predict motion accurately in both fully observed and partially observed scenarios.
  arXiv  Detail & Related papers  (2025-01-28T07:46:13Z)
• 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)
• 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)
• Towards Theoretical Understandings of Self-Consuming Generative Models [56.84592466204185]
  This paper tackles the emerging challenge of training generative models within a self-consuming loop. We construct a theoretical framework to rigorously evaluate how this training procedure impacts the data distributions learned by future models. We present results for kernel density estimation, delivering nuanced insights such as the impact of mixed data training on error propagation.
  arXiv  Detail & Related papers  (2024-02-19T02:08:09Z)
• GEO-Bench: Toward Foundation Models for Earth Monitoring [139.77907168809085]
  We propose a benchmark comprised of six classification and six segmentation tasks. This benchmark will be a driver of progress across a variety of Earth monitoring tasks.
  arXiv  Detail & Related papers  (2023-06-06T16:16:05Z)
• TRAK: Attributing Model Behavior at Scale [79.56020040993947]
  We present TRAK (Tracing with Randomly-trained After Kernel), a data attribution method that is both effective and computationally tractable for large-scale, differenti models.
  arXiv  Detail & Related papers  (2023-03-24T17:56:22Z)
• 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)
• The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models [115.49214555402567]
  Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Recent studies suggest that pre-training benefits from gigantic model capacity. In this paper, we examine supervised and self-supervised pre-trained models through the lens of the lottery ticket hypothesis (LTH)
  arXiv  Detail & Related papers  (2020-12-12T21:53:55Z)
• The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations [0.0]
  We investigate the composition of training data in vehicle trajectory prediction. We show that the models employing our semantic vector representation outperform the numerical model when trained on an adequate data set.
  arXiv  Detail & Related papers  (2020-09-30T20:00:11Z)
• TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning [9.01310450044549]
  This research suggests a generative modeling approach to learn the underlying distributions of urban vehicle trajectory data. A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data. TrajGAIL is a generative adversarial imitation learning framework for the urban vehicle trajectory generation.
  arXiv  Detail & Related papers  (2020-07-28T13:17:51Z)
• Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction [10.905596145969223]
  We propose a simple encoder-decoder architecture based on multi-head attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel.
  arXiv  Detail & Related papers  (2020-04-08T06:58:51Z)

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.