TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning

• URL: http://arxiv.org/abs/2007.14189v4
• Date: Sat, 16 Jan 2021 02:41:58 GMT
• Title: TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning
• Authors: Seongjin Choi, Jiwon Kim, Hwasoo Yeo
• Abstract summary: 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.
• Score: 9.01310450044549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, an abundant amount of urban vehicle trajectory data has been collected in road networks. Many studies have used machine learning algorithms to analyze patterns in vehicle trajectories to predict location sequences of individual travelers. Unlike the previous studies that used a discriminative modeling approach, 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 and, thus, produce synthetic vehicle trajectories similar to real vehicle trajectories with limited observations. Synthetic trajectories can provide solutions to data sparsity or data privacy issues in using location data. This research proposesTrajGAIL, a generative adversarial imitation learning framework for the urban vehicle trajectory generation. In TrajGAIL, learning location sequences in observed trajectories is formulated as an imitation learning problem in a partially observable Markov decision process. The model is trained by the generative adversarial framework, which uses the reward function from the adversarial discriminator. The model is tested with both simulation and real-world datasets, and the results show that the proposed model obtained significant performance gains compared to existing models in sequence modeling.

Related papers

• VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning [9.325787573209201]
  Trajectory similarity search plays an essential role in autonomous driving. We propose VeTraSS -- an end-to-end pipeline for Vehicle Trajectory Similarity Search.
  arXiv  Detail & Related papers  (2024-04-11T06:19:55Z)
• 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)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• 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)
• 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)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving [19.06020265777298]
  Existing simulators rely on system-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios. We propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic behaviors directly from human demonstration. In addition, we develop a data-driven simulator I-Sim that can be used to train reinforcement learning models in parallel based on naturalistic driving data.
  arXiv  Detail & Related papers  (2022-03-31T04:48:29Z)
• 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)
• Pedestrian Trajectory Prediction with Convolutional Neural Networks [0.3787359747190393]
  We propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques.
  arXiv  Detail & Related papers  (2020-10-12T15:51:01Z)
• 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)

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.