This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Free-Positioned Smartphone Sensing for Vehicle Dynamics Estimation
Technical Paper
2017-01-0072
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
With the embedded sensors – typically Inertial Measurement Units (IMU) and GPS, the smartphone could be leveraged as a low-cost sensing platform for estimating vehicle dynamics. However, the orientation and relative movement of the smartphone inside the vehicle yields the main challenge for platform deployment. This study proposes a solution of converting the smartphone-referenced IMU readings into vehicle-referenced accelerations, which allows free-positioned smartphone for the in-vehicle dynamics sensing. The approach is consisted of (i) geometry coordinate transformation techniques, (ii) neural networks regression of IMU from GPS, and (iii) adaptive filtering processes. Experiment is conducted in three driving environments which cover high occurrence of vehicle dynamic movements in lateral, longitudinal, and vertical directions. The processing effectiveness at five typical positions (three fixed and two flexible) are examined. Results are quantified as the normalized cross-correlation ratio, comparing a free-positioned device against a well-aligned device. The conversion of vertical acceleration is more successful, whereas the lateral and longitudinal accelerations processing outputs may vary. The coordinate transformation completes the most conversion process, and regression and filtering make additional adjustment. After discussion, a final implementation processing pipeline is suggested for the deployment of real-time system.
Recommended Content
Authors
Topic
Citation
Zheng, Y., Shokouhi, N., Sathyanarayana, A., and Hansen, J., "Free-Positioned Smartphone Sensing for Vehicle Dynamics Estimation," SAE Technical Paper 2017-01-0072, 2017, https://doi.org/10.4271/2017-01-0072.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Engelbrecht , J. , Booysen , M. J. , van Rooyen , G. J. , & Bruwer , F. J. Survey of smartphone-based sensing in vehicles for intelligent transportation system applications Intelligent Transport Systems IET 9.10 2015 924 935
- Johnson , Derick A. , and Trivedi Mohan M. Driving style recognition using a smartphone as a sensor platform Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on IEEE 2011
- Eren , H. , Makinist , S. , Akin , E. , & Yilmaz , A. Estimating driving behavior by a smartphone Intelligent Vehicles Symposium (IV), 2012 IEEE IEEE 2012
- Eriksson , J. , Girod , L. , Hull , B. , Newton , R. , Madden , S. , & Balakrishnan , H. The pothole patrol: using a mobile sensor network for road surface monitoring Proceedings of the 6th international conference on Mobile systems, applications, and services ACM 2008
- Dai , J. , Teng , J. , Bai , X. , Shen , Z. , & Xuan , D. Mobile phone based drunk driving detection Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2010 4th International Conference on-NO PERMISSIONS IEEE 2010
- Almazan , J. , Bergasa , L. M. , Yebes , J. J. , Barea , R. , & Arroyo , R. Full auto-calibration of a smartphone on board a vehicle using IMU and GPS embedded sensors Intelligent Vehicles Symposium (IV) IEEE 2013
- Castignani , G. , Derrmann , T. , Frank , R. , & Engel , T. Driver behavior profiling using smartphones: A low-cost platform for driver monitoring Intelligent Transportation Systems Magazine IEEE 7.1 2015 91 102
- Mohan , Prashanth , Padmanabhan Venkata N. , and Ramjee Ramachandran Nericell: rich monitoring of road and traffic conditions using mobile smartphones Proceedings of the 6th ACM conference on Embedded network sensor systems ACM 2008
- Wahlstrom , J. , Skog , I. , Handel , P. , & Nehorai , A. IMU-based Smartphone-to-Vehicle Positioning IEEE Transactions on Intelligent Vehicles 99 2016
- Sathyanarayana A. , Sadjadi S.O. , Hansen J.H.L. Leveraging Sensor Information from Portable Devices Towards Automatic Driving Maneuver Recognition 15th IEEE ITSC Anchorage, AK, USA Sep. 2012 660 665
- Zheng , Y. , Shi , X. , Sathyanarayana , A. , Shokouhi , N. , & Hansen , J. H.L. In-vehicle speech recognition and tutorial keywords spotting for novice drivers' performance evaluation Intelligent Vehicles Symposium (IV), 2015 IEEE IEEE 2015
- Zheng , Y. , Shokouhi , N. , Thomsen , N. , Sathyanarayana , A. et al. Towards Developing a Distraction-Reduced Hands-Off Interactive Driving Experience using Portable Smart Devices SAE Technical Paper 2016-01-0140 2016 10.4271/2016-01-0140
- Zheng , Y. , Hansen , J.H.L. Unsupervised Driving Performance Assessment using Free-Positioned Smartphones in Vehicles IEEE Inter. Conf. Intel. Transportation Systems Conf. Rio de Janeiro, Brazil Nov. 1-4, 2016
- Wang , Y. , Yang , J. , Liu , H. , Chen , Y. , Gruteser , M. , & Martin , R. P. Sensing vehicle dynamics for determining driver phone use Proceeding of the 11th annual international conference on Mobile systems, applications, and services ACM 2013
- Widnall , S. Lecture L3-Vectors, Matrices and Coordinate Transformations Dynamics 2009
- Arulampalam , G. , and Bouzerdoum , A. A generalized feedforward neural network architecture for classification and regression Neural networks 16 5 2003 561 568
- Multi-Layer Perceptrons – scikit neural network library Oct. 02 2016 http://scikit-neuralnetwork.readthedocs.io/en/latest/module_mlp.html
- Gollamudi , S. , Nagaraj , S. , Kapoor , S. , and Huang , Y.F. Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size IEEE Signal Processing Letters 5 5 1998 111 114
- Wan , E.A. , and Van Der Merwe , R. The unscented Kalman filter for nonlinear estimation In Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000 153 158 IEEE 2000
- Zheng , Y. , and Hansen , J.H.L MobileUTDrive: An Android Portable Device Platform for In-vehicle Driving Data Collection and Display FAST-zero’15 Gothenburg, Sweden September 9-11, 2015