OASIS: Automated Assessment of Urban Pedestrian Paths at Scale

• URL: http://arxiv.org/abs/2303.02287v2
• Date: Thu, 4 May 2023 20:03:14 GMT
• Title: OASIS: Automated Assessment of Urban Pedestrian Paths at Scale
• Authors: Yuxiang Zhang, Suresh Devalapalli, Sachin Mehta, Anat Caspi
• Abstract summary: We develop a free and open-source automated mapping system to extract sidewalk network data using mobile physical devices. We describe a prototype system trained and tested with imagery collected in real-world settings, alongside human surveyors who are part of the local transit pathway review team.
• Score: 16.675093530600154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The inspection of the Public Right of Way (PROW) for accessibility barriers is necessary for monitoring and maintaining the built environment for communities' walkability, rollability, safety, active transportation, and sustainability. However, an inspection of the PROW, by surveyors or crowds, is laborious, inconsistent, costly, and unscalable. The core of smart city developments involves the application of information technologies toward municipal assets assessment and management. Sidewalks, in comparison to automobile roads, have not been regularly integrated into information systems to optimize or inform civic services. We develop an Open Automated Sidewalks Inspection System (OASIS), a free and open-source automated mapping system, to extract sidewalk network data using mobile physical devices. OASIS leverages advances in neural networks, image sensing, location-based methods, and compact hardware to perform sidewalk segmentation and mapping along with the identification of barriers to generate a GIS pedestrian transportation layer that is available for routing as well as analytic and operational reports. We describe a prototype system trained and tested with imagery collected in real-world settings, alongside human surveyors who are part of the local transit pathway review team. Pilots show promising precision and recall for path mapping (0.94, 0.98 respectively). Moreover, surveyor teams' functional efficiency increased in the field. By design, OASIS takes adoption aspects into consideration to ensure the system could be easily integrated with governmental pathway review teams' workflows, and that the outcome data would be interoperable with public data commons.

Related papers

• Reliable, Routable, and Reproducible: Collection of Pedestrian Pathways at Statewide Scale [7.346075203371274]
  This paper presents a methodology to collect, manage, serve, and maintain pedestrian path data at a statewide scale. We aim to produce routable pedestrian pathways for the entire State of Washington within approximately two years.
  arXiv  Detail & Related papers  (2024-10-12T02:31:57Z)
• Leveraging GNSS and Onboard Visual Data from Consumer Vehicles for Robust Road Network Estimation [18.236615392921273]
  This paper addresses the challenge of road graph construction for autonomous vehicles. We propose using global navigation satellite system (GNSS) traces and basic image data acquired from these standard sensors in consumer vehicles. We exploit the spatial information in the data by framing the problem as a road centerline semantic segmentation task using a convolutional neural network.
  arXiv  Detail & Related papers  (2024-08-03T02:57:37Z)
• Towards a real-time distributed feedback system for the transportation assistance of PwD [0.0]
  The proposed design allows the addition of further extensions regarding the assistance of individuals with mobility problems. The main components of the proposed architecture include the Inaccessible City Point System, the Live Data Analysis and Response System, and the Obstruction Detection and Prevention System.
  arXiv  Detail & Related papers  (2024-06-10T10:31:39Z)
• MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles [58.461077944514564]
  This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
  arXiv  Detail & Related papers  (2023-10-08T21:32:30Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• A sequential transit network design algorithm with optimal learning under correlated beliefs [4.8951183832371]
  This study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning to address the operation under limited data. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City.
  arXiv  Detail & Related papers  (2023-05-16T14:14:51Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications [8.633120731620307]
  We propose a novel collaborative 3D object detection framework that consists of three components. Experiment results and bandwidth usage analysis demonstrate that our approach can save communication and computation costs.
  arXiv  Detail & Related papers  (2022-05-24T07:17:32Z)
• AI in Smart Cities: Challenges and approaches to enable road vehicle automation and smart traffic control [56.73750387509709]
  SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
  arXiv  Detail & Related papers  (2021-04-07T14:31:08Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• 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)

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.