FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing

• URL: http://arxiv.org/abs/2007.08551v2
• Date: Thu, 28 Jan 2021 01:05:04 GMT
• Title: FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing
• Authors: Wei Shao, Sichen Zhao, Zhen Zhang, Shiyu Wang, Mohammad Saiedur Rahaman, Andy Song, Flora Dilys Salim
• Abstract summary: We design an end-to-end transfer learning framework for parking availability sensing to predict parking occupancy in areas in which the parking data is insufficient to feed into data-hungry models. This framework overcomes two challenges: 1) many real-world cases cannot provide enough data for most existing data-driven models, and 2) it is difficult to merge sensor data and heterogeneous contextual information.
• Score: 5.160087162892865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing research on parking availability sensing mainly relies on extensive contextual and historical information. In practice, the availability of such information is a challenge as it requires continuous collection of sensory signals. In this study, we design an end-to-end transfer learning framework for parking availability sensing to predict parking occupancy in areas in which the parking data is insufficient to feed into data-hungry models. This framework overcomes two main challenges: 1) many real-world cases cannot provide enough data for most existing data-driven models, and 2) it is difficult to merge sensor data and heterogeneous contextual information due to the differing urban fabric and spatial characteristics. Our work adopts a widely-used concept, adversarial domain adaptation, to predict the parking occupancy in an area without abundant sensor data by leveraging data from other areas with similar features. In this paper, we utilise more than 35 million parking data records from sensors placed in two different cities, one a city centre and the other a coastal tourist town. We also utilise heterogeneous spatio-temporal contextual information from external resources, including weather and points of interest. We quantify the strength of our proposed framework in different cases and compare it to the existing data-driven approaches. The results show that the proposed framework is comparable to existing state-of-the-art methods and also provide some valuable insights on parking availability prediction.

Related papers

• Leverage Multi-source Traffic Demand Data Fusion with Transformer Model for Urban Parking Prediction [4.672121078249809]
  This study proposes a parking availability prediction framework integrating spatial-temporal deep learning with multi-source data fusion. The framework is based on the Transformer as the spatial-temporal deep learning model. Real-world empirical data was used to verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-05-02T07:28:27Z)
• Collaborative Perception Datasets in Autonomous Driving: A Survey [0.0]
  The paper systematically analyzes a variety of datasets, comparing them based on aspects such as diversity, sensor setup, quality, public availability, and their applicability to downstream tasks. The importance of addressing privacy and security concerns in the development of datasets is emphasized, regarding data sharing and dataset creation.
  arXiv  Detail & Related papers  (2024-04-22T09:36:17Z)
• UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction [93.77809355002591]
  We introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria. We conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. We provide insights into dataset characteristics to explain these findings.
  arXiv  Detail & Related papers  (2024-03-22T10:36:50Z)
• Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems [3.00363876980149]
  Intelligent parking monitoring is still a challenge since most approaches involve collecting and labeling large amounts of data. Our study aims to uncover the challenges in creating a global framework, trained using publicly available labeled parking lot images. We found that models trained on diverse datasets can achieve 95% accuracy without the burden of data annotation and model training on the target parking lot.
  arXiv  Detail & Related papers  (2023-09-28T14:59:53Z)
• LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting [65.71129509623587]
  Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning. However, the promising results achieved on current public datasets may not be applicable to practical scenarios. We introduce the LargeST benchmark dataset, which includes a total of 8,600 sensors in California with a 5-year time coverage.
  arXiv  Detail & Related papers  (2023-06-14T05:48:36Z)
• IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes [79.18349050238413]
  Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models. We build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames.
  arXiv  Detail & Related papers  (2022-10-23T23:03:17Z)
• Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception [59.2014692323323]
  Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them. We propose a novel, end-to-end trainable Hindsight framework to extract contextual information from past data. We show that this framework is compatible with most modern 3D detection architectures and can substantially improve their average precision on multiple autonomous driving datasets.
  arXiv  Detail & Related papers  (2022-03-22T00:58:27Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Towards automatic extraction and validation of on-street parking spaces using park-out events data [0.0]
  We propose two approaches to automatically create a map for valid on-street car parking spaces. The first one uses spatial aggregation and the second a machine learning algorithm. We show our results for a neighborhood in the city of Berlin and report a classification accuracy of 92% on the original imbalanced data.
  arXiv  Detail & Related papers  (2021-02-12T20:22:38Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• IDDA: a large-scale multi-domain dataset for autonomous driving [16.101248613062292]
  This paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions.
  arXiv  Detail & Related papers  (2020-04-17T15:22:38Z)

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.