DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction

• URL: http://arxiv.org/abs/2403.08055v1
• Date: Tue, 12 Mar 2024 20:02:39 GMT
• Title: DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction
• Authors: Mohamed Elrefaie, Angela Dai, Faez Ahmed
• Abstract summary: This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model. Together, DrivAerNet and RegDGCNN promise to accelerate the car design process and contribute to the development of more efficient vehicles.
• Score: 30.697742505713254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model, both aimed at aerodynamic car design through machine learning. DrivAerNet, with its 4000 detailed 3D car meshes using 0.5 million surface mesh faces and comprehensive aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses, addresses the critical need for extensive datasets to train deep learning models in engineering applications. It is 60\% larger than the previously available largest public dataset of cars, and is the only open-source dataset that also models wheels and underbody. RegDGCNN leverages this large-scale dataset to provide high-precision drag estimates directly from 3D meshes, bypassing traditional limitations such as the need for 2D image rendering or Signed Distance Fields (SDF). By enabling fast drag estimation in seconds, RegDGCNN facilitates rapid aerodynamic assessments, offering a substantial leap towards integrating data-driven methods in automotive design. Together, DrivAerNet and RegDGCNN promise to accelerate the car design process and contribute to the development of more efficient vehicles. To lay the groundwork for future innovations in the field, the dataset and code used in our study are publicly accessible at \url{https://github.com/Mohamedelrefaie/DrivAerNet}

Related papers

• DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks [25.00264553520033]
  DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification.
  arXiv  Detail & Related papers  (2024-06-13T23:19:48Z)
• DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving [67.46481099962088]
  Current vision-centric pre-training typically relies on either 2D or 3D pre-text tasks, overlooking the temporal characteristics of autonomous driving as a 4D scene understanding task. We introduce emphcentricDriveWorld, which is capable of pre-training from multi-camera driving videos in atemporal fashion. DriveWorld delivers promising results on various autonomous driving tasks.
  arXiv  Detail & Related papers  (2024-05-07T15:14:20Z)
• TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction [0.0]
  Trajectory prediction is a critical component of autonomous driving systems. This paper introduces TrajectoryNAS, a pioneering method that focuses on utilizing point cloud data for trajectory prediction.
  arXiv  Detail & Related papers  (2024-03-18T11:48:41Z)
• AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration [69.21282992341007]
  Auto Synth automatically generates 3D training data for point cloud registration. We replace the point cloud registration network with a much smaller surrogate network, leading to a $4056.43$ speedup. Our results on TUD-L, LINEMOD and Occluded-LINEMOD evidence that a neural network trained on our searched dataset yields consistently better performance than the same one trained on the widely used ModelNet40 dataset.
  arXiv  Detail & Related papers  (2023-09-20T09:29:44Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [64.16991399882477]
  We aim to tackle the challenge of learning general trajectory forecasting representations under limited data availability. We take advantage of graph representations of HD-map and apply vector transformations to reshape the maps. We employ a rule-based model to generate trajectories based on augmented scenes.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety [3.3659635625913564]
  We have developed a dataset for short-term trajectory prediction tasks using the CARLA simulator. This dataset is extensive and incorporates what is considered complex scenarios - pedestrians crossing the road, vehicles overtaking. An end-to-end short-term trajectory prediction model using convolutional neural networks (CNN) and long short-term memory (LSTM) networks has also been developed.
  arXiv  Detail & Related papers  (2023-07-11T14:28:33Z)
• Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings [4.868319717279586]
  We propose a new two-dimensional (2D) representation of 3D shapes to verify its effectiveness in predicting 3D car drag. We construct a diverse dataset of 9,070 high-quality 3D car meshes labeled by drag coefficients. Our experiments demonstrate that our model can accurately and efficiently evaluate drag coefficients with an $R2$ value above 0.84 for various car categories.
  arXiv  Detail & Related papers  (2023-05-26T09:33:12Z)
• DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction [7.476566278759198]
  By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot attention in AI and Intelligent Transportation System community. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and time-series models.
  arXiv  Detail & Related papers  (2021-08-20T10:08:26Z)
• PC-DAN: Point Cloud based Deep Affinity Network for 3D Multi-Object Tracking (Accepted as an extended abstract in JRDB-ACT Workshop at CVPR21) [68.12101204123422]
  A point cloud is a dense compilation of spatial data in 3D coordinates. We propose a PointNet-based approach for 3D Multi-Object Tracking (MOT)
  arXiv  Detail & Related papers  (2021-06-03T05:36:39Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)

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.