PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting

• URL: http://arxiv.org/abs/2412.06491v2
• Date: Wed, 30 Apr 2025 18:34:20 GMT
• Title: PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
• Authors: Yihong Xu, Yuan Yin, Éloi Zablocki, Tuan-Hung Vu, Alexandre Boulch, Matthieu Cord,
• Abstract summary: State-of-the-art motion forecasting models rely on large curated datasets with manually annotated or heavily post-processed trajectories.<n>PWT is a simple and scalable alternative that uses unprocessed and diverse trajectories automatically generated from off-the-shelf 3D detectors and tracking.<n>It achieves strong performance across standard benchmarks particularly in low-data regimes, and in cross-domain, end-to-end and multi-class settings.
• Score: 90.47748423913369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately predicting how agents move in dynamic scenes is essential for safe autonomous driving. State-of-the-art motion forecasting models rely on large curated datasets with manually annotated or heavily post-processed trajectories. However, building these datasets is costly, generally manual, hard to scale, and lacks reproducibility. They also introduce domain gaps that limit generalization across environments. We introduce PPT (Pretraining with Pseudo-labeled Trajectories), a simple and scalable alternative that uses unprocessed and diverse trajectories automatically generated from off-the-shelf 3D detectors and tracking. Unlike traditional pipelines aiming for clean, single-label annotations, PPT embraces noise and diversity as useful signals for learning robust representations. With optional finetuning on a small amount of labeled data, models pretrained with PPT achieve strong performance across standard benchmarks particularly in low-data regimes, and in cross-domain, end-to-end and multi-class settings. PPT is easy to implement and improves generalization in motion forecasting. Code and data will be released upon acceptance.

Related papers

• Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps [8.897884780881535]
  LiDAR-based 3D object detectors often fail to adapt well to target domains with different sensor configurations. Recent studies suggest that pre-trained backbones can be learned in a self-supervised manner with large-scale unlabeled LiDAR frames. We propose a novel method, called Domain Adaptive Distill-Tuning (DADT), to adapt a pre-trained model with limited target data.
  arXiv  Detail & Related papers  (2024-10-02T08:22:42Z)
• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• 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)
• DPTraj-PM: Differentially Private Trajectory Synthesis Using Prefix Tree and Markov Process [1.800676987432211]
  We propose DPTraj-PM, a method to synthesize trajectory dataset under the differential privacy framework. DPTraj-PM discretizes the raw trajectories into neighboring cells, and models them by combining a prefix tree structure and an m-order Markov process. The output traces crafted by DPTraj-PM not only preserves the patterns and variability in individuals' mobility behaviors, but also protects individual privacy.
  arXiv  Detail & Related papers  (2024-04-22T11:52:11Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations [76.45009891152178]
  Pretraining-finetuning approach can alleviate the labeling burden by fine-tuning a pre-trained backbone across various downstream datasets as well as tasks. We show, for the first time, that general representations learning can be achieved through the task of occupancy prediction. Our findings will facilitate the understanding of LiDAR points and pave the way for future advancements in LiDAR pre-training.
  arXiv  Detail & Related papers  (2023-09-19T11:13:01Z)
• 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)
• Efficient Baselines for Motion Prediction in Autonomous Driving [7.608073471097835]
  Motion Prediction (MP) of multiple surroundings agents is a crucial task in arbitrarily complex environments. We aim to develop compact models using State-Of-The-Art (SOTA) techniques for MP, including attention mechanisms and GNNs.
  arXiv  Detail & Related papers  (2023-09-06T22:18:16Z)

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.