Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review

• URL: http://arxiv.org/abs/2411.07315v2
• Date: Wed, 13 Nov 2024 17:28:15 GMT
• Title: Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review
• Authors: Amith Khandakar, David G. Michelson, Mansura Naznine, Abdus Salam, Md. Nahiduzzaman, Khaled M. Khan, Ponnuthurai Nagaratnam Suganthan, Mohamed Arselene Ayari, Hamid Menouar, Julfikar Haider,
• Abstract summary: This study introduces a comprehensive dataset derived from smartphone sensors. These sensors capture parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning.
• Score: 7.867406170788454
• License:
• Abstract: Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns. This study addresses this gap by introducing a comprehensive dataset derived from smartphone sensors, which surpasses existing datasets by incorporating a diverse range of sensors including accelerometer, gyroscope, magnetometer, GPS, gravity, orientation, and uncalibrated sensors. These sensors capture extensive parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed, providing a detailed understanding of road conditions and driving behaviors. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning. By making this dataset available to the community, the study aims to foster collaboration, inspire further research, and facilitate the development of innovative solutions in intelligent transportation systems.

Related papers

• The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition [136.32656319458158]
  The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies. This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries. The competition culminated in 15 top-performing solutions.
  arXiv  Detail & Related papers  (2024-05-14T17:59:57Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems [0.0]
  This study delves into the application of cutting-edge technological methods in smart cities. We present a novel comprehensive dataset for traffic accident detection. This dataset is expected to advance academic research and also enhance real-time accident detection applications.
  arXiv  Detail & Related papers  (2024-01-07T21:50:24Z)
• The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset [4.413278371057897]
  Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. We use an intelligent public inner-city intersection in Germany with visual sensor technology. The resulting dataset consists of eight hours of measurement data.
  arXiv  Detail & Related papers  (2023-07-12T13:46:20Z)
• Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving Applications [2.0502751783060003]
  We present a novel approach to perform detailed quality assessment for smart roadside infrastructure sensors. Our framework is multimodal across different sensor types and is evaluated on the DAIR-V2X dataset.
  arXiv  Detail & Related papers  (2023-04-16T10:21:07Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Smartphone-based Hard-braking Event Detection at Scale for Road Safety Services [6.451490979743455]
  Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. This paper presents a scalable approach for detecting hard-braking events using the kinematics data collected from smartphone sensors. We train a Transformer-based machine learning model for hard-braking event detection using concurrent sensor readings from smartphones and vehicle sensors from drivers who connect their phone to the vehicle while navigating in Google Maps.
  arXiv  Detail & Related papers  (2022-02-04T01:30:32Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• 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)
• LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
  We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors. LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs. It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
  arXiv  Detail & Related papers  (2020-03-13T06:17:39Z)

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.