Action-based Contrastive Learning for Trajectory Prediction

• URL: http://arxiv.org/abs/2207.08664v1
• Date: Mon, 18 Jul 2022 15:02:27 GMT
• Title: Action-based Contrastive Learning for Trajectory Prediction
• Authors: Marah Halawa, Olaf Hellwich, Pia Bideau
• Abstract summary: Trajectory prediction is an essential task for successful human robot interaction, such as in autonomous driving. In this work, we address the problem of predicting future pedestrian trajectories in a first person view setting with a moving camera. We propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings.
• Score: 4.675212251005813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trajectory prediction is an essential task for successful human robot interaction, such as in autonomous driving. In this work, we address the problem of predicting future pedestrian trajectories in a first person view setting with a moving camera. To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings. The fundamental idea behind this new loss is that trajectories of pedestrians performing the same action should be closer to each other in the feature space than the trajectories of pedestrians with significantly different actions. In other words, we argue that behavioral information about pedestrian action influences their future trajectory. Furthermore, we introduce a novel sampling strategy for trajectories that is able to effectively increase negative and positive contrastive samples. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian behavior, i.e. action, effectively, and it learns a better trajectory representation. Thus, integrating the proposed contrastive framework within a trajectory prediction model improves its results and outperforms state-of-the-art methods on three trajectory prediction benchmarks [31, 32, 26].

Related papers

• Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction [3.522062800701924]
  We introduce PTINet, which learns trajectory and intention prediction by combining past trajectory observations, local contextual features, and global features. The efficacy of our approach is evaluated on widely used public datasets: JAAD and PIE. PTINet paves the way for the development of automated systems capable of seamlessly interacting with pedestrians in urban settings.
  arXiv  Detail & Related papers  (2024-07-24T11:06:47Z)
• LG-Traj: LLM Guided Pedestrian Trajectory Prediction [9.385936248154987]
  We introduce LG-Traj, a novel approach to generate motion cues present in pedestrian past/observed trajectories. These motion cues, along with pedestrian coordinates, facilitate a better understanding of the underlying representation. Our method employs a transformer-based architecture comprising a motion encoder to model motion patterns and a social decoder to capture social interactions among pedestrians.
  arXiv  Detail & Related papers  (2024-03-12T19:06:23Z)
• 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)
• Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving [13.126949982768505]
  The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high risk for driving. This work presents a comparative evaluation of trajectory-only and appearance-based methods for pedestrian prediction.
  arXiv  Detail & Related papers  (2023-05-25T11:24:38Z)
• AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
  Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors. We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories. Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
  arXiv  Detail & Related papers  (2022-09-19T03:34:59Z)
• 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)
• Human Trajectory Prediction via Counterfactual Analysis [87.67252000158601]
  Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots. Most existing methods learn to predict future trajectories by behavior clues from history trajectories and interaction clues from environments. We propose a counterfactual analysis method for human trajectory prediction to investigate the causality between the predicted trajectories and input clues.
  arXiv  Detail & Related papers  (2021-07-29T17:41:34Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Social NCE: Contrastive Learning of Socially-aware Motion Representations [87.82126838588279]
  Experimental results show that the proposed method dramatically reduces the collision rates of recent trajectory forecasting, behavioral cloning and reinforcement learning algorithms. Our method makes few assumptions about neural architecture designs, and hence can be used as a generic way to promote the robustness of neural motion models.
  arXiv  Detail & Related papers  (2020-12-21T22:25:06Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.