Low-Cost Realtime Horizontal Curve Detection Using Inertial Sensors of a Smartphone

  title={Low-Cost Realtime Horizontal Curve Detection Using Inertial Sensors of a Smartphone},
  author={Shaohu Zhang and Myounggyu Won and Sang Hyuk Son},
  journal={2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)},
  • Shaohu Zhang, M. Won, S. Son
  • Published 1 September 2016
  • Computer Science
  • 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)
Fatal accidents occur frequently on low-volume rural roads, and the accident rates are up to 4 times higher at curves. It is thus of paramount importance to perform road inventory of rural roads to develop safety plans. However, most states in U.S. face a challenge to maintain a database for low-volume rural roads due to limited funds for road inventory. In this paper, we propose to significantly reduce the cost for road inventory specifically focusing on horizontal curve detection by… 
Identification and Calculation of Horizontal Curves for Low-Volume Roadways Using Smartphone Sensors
A low-cost mobile road inventory system for two-lane horizontal curves based on off-the-shelf smartphones capable of accurately detecting horizontal curves by exploiting a K-means machine learning technique and achieves high curve identification accuracy as well as high accuracy for calculating curve radius and superelevation.
Pavement Management Utilizing Mobile Crowd Sensing
The process of pavement anomalies detection based on inertial data was reviewed in detail, including preparatory, data collection, and processing phases of the previous experiments, and some of the key issues in the experimental phases were investigated by previous studies, while some other challenges were not tackled or noticed.
Identification, Calculation and Warning of Horizontal Curves for Low-volume Two-lane Roadways Using Smartphone Sensors


Automated Extraction of Horizontal Curve Information for Low-Volume Roads
Rates of fatal and injury crashes on low-volume roads are much higher than those on higher-volume roads. A large proportion of crashes on low-volume roads are roadway departure crashes. Research has
Using mobile phone barometer for low-power transportation context detection
This is the first paper that uses only the barometer for context detection, which uses 32 mW lower power compared to Google, and has comparable accuracy to both Google and FMS.
Invisible Sensing of Vehicle Steering with Smartphones
The extensive evaluation results show that V-Sense is accurate in determining and differentiating various steering maneuvers, and is thus useful for a wide range of safety-assistance applications without additional sensors or infrastructure.
Avoiding Roadway Departure Crashes with an In-Vehicle Head-Up Display
This study proposes a method of warning drivers of horizontal curves in order to prevent motor vehicles from running off the road and the overall results indicate relatively high accuracy.
Mining users' significant driving routes with low-power sensors
A passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer that can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit is presented.
Automatic Horizontal Curve Identification and Measurement Method Using GPS Data
This study is aimed to develop a new method using widely available GPS data that can automaticly identify complex curves in the network-level analysis of roadway geometry design.
Workshops on using the GPS method to determine curve advisory speeds.
Curve warning signs are intended to improve curve safety by alerting the driver to a change in geometry that may not be apparent or expected. However, several research projects conducted in the last
Horizontal Curve Identification and Evaluation
Horizontal curves are over-represented, high-frequency, high-severity crash locations. Significant opportunities exist to mitigate these crashes through relatively low-cost safety improvements such
An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery
  • W. Shi, Z. Miao, J. Debayle
  • Mathematics, Environmental Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2014
An integrated method to extract urban main-road centerlines from satellite optical images using general adaptive neighborhood to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups is presented.
Approximate Extraction of Spiralled Horizontal Curves from Satellite Imagery
Generating road databases from high-resolution satellite imagery is advantageous over traditional methods because of its simplicity and efficiency. Previous research has addressed the extraction of