AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data

• URL: http://arxiv.org/abs/2412.18038v1
• Date: Mon, 23 Dec 2024 23:17:44 GMT
• Title: AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
• Authors: Mirko Zaffaroni, Federico Signoretta, Marco Grangetto, Attilio Fiandrotti,
• Abstract summary: We propose a method to augment synthetic trajectories at training time and with an adversarial approach. We show that trajectory augmentation at training time unleashes significant gains when a state-of-the-art generative model is evaluated over real-world trajectories.
• Score: 9.108224187521287
• License:
• Abstract: Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. Deep generative models achieve top performance in this task, assuming enough labelled trajectories are available for training. To this end, large amounts of synthetically generated, labelled trajectories exist (e.g., generated by video games). However, such trajectories are not meant to represent pedestrian motion realistically and are ineffective at training a predictive model. We propose a method and an architecture to augment synthetic trajectories at training time and with an adversarial approach. We show that trajectory augmentation at training time unleashes significant gains when a state-of-the-art generative model is evaluated over real-world trajectories.

Related papers

• Extrapolated Urban View Synthesis Benchmark [53.657271730352214]
  Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs) At their core is Novel View Synthesis (NVS), a crucial capability that generates diverse unseen viewpoints to accommodate the broad and continuous pose distribution of AVs. Recent advances in radiance fields, such as 3D Gaussian Splatting, achieve photorealistic rendering at real-time speeds and have been widely used in modeling large-scale driving scenes. We have released our data to help advance self-driving and urban robotics simulation technology.
  arXiv  Detail & Related papers  (2024-12-06T18:41:39Z)
• KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation [17.095651262950568]
  Trajectory generation is a pivotal task in autonomous driving. Recent studies have introduced the autoregressive paradigm. We propose the Kinematic-Driven Generative Model for Realistic Agent Simulation.
  arXiv  Detail & Related papers  (2024-07-17T18:12:11Z)
• DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction [7.346307332191997]
  We present a novel framework that leverages diffusion models for predicting future trajectories in a computationally efficient manner. We employ an efficient sampling mechanism that allows us to maximize the number of sampled trajectories for improved accuracy. We show the effectiveness of our approach by conducting empirical evaluations on common pedestrian (UCY/ETH) and autonomous driving (nuScenes) benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-23T05:04:23Z)
• 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)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Deep Generative Models for Vehicle Speed Trajectories [2.5137859989323537]
  We show how extensions to deep generative models allow accurate and scalable generation. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.
  arXiv  Detail & Related papers  (2021-12-14T20:14:03Z)
• Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction [11.241614693184323]
  We present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. We show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset.
  arXiv  Detail & Related papers  (2021-07-01T13:08:31Z)
• PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning [84.30765628008207]
  We propose a novel method, dubbed PlayVirtual, which augments cycle-consistent virtual trajectories to enhance the data efficiency for RL feature representation learning. Our method outperforms the current state-of-the-art methods by a large margin on both benchmarks.
  arXiv  Detail & Related papers  (2021-06-08T07:37:37Z)
• 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)
• Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data [37.176411554794214]
  Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. We present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents. We demonstrate its performance on several challenging real-world trajectory forecasting datasets.
  arXiv  Detail & Related papers  (2020-01-09T16:47:17Z)

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.