Location Detection of Vehicular Accident Using Global Navigation Satellite Systems/Inertial Measurement Units Navigator
Vehicle tracking and accident recognizing are considered by many industries like insurance and vehicle rental companies. The main goal of this paper is to detect the location of a car accident by combining different methods. The methods, which are considered in this paper, are Global Navigation Satellite Systems/Inertial Measurement Units (GNSS/IMU)-based navigation and vehicle accident detection algorithms. They are expressed by a set of raw measurements, which are obtained from a designed integrator black box using GNSS and inertial sensors. Another concern of this paper is the definition of accident detection algorithm based on its jerk to identify the position of that accident. In fact, the results convinced us that, even in GNSS blockage areas, the position of the accident could be detected by GNSS/INS integration with 50% improvement compared to GNSS stand alone.
 Consulting, R. S. C., Road safty in Canada. 2011, Public Health Agency of Canada. p. 44.
 Landry, R. J., Vehicle Tracking and Accident Diagnostic System (VTADS). 2012.
 Li, N. and C. Busso. Analysis of facial features of drivers under cognitive and visual distractions. in Multimedia and Expo (ICME), 2013 IEEE International Conference on. 2013. IEEE.
 Zongyuan, W., et al. The study of square root cubature Kalman smoother and its application on INS/GPS integrated navigation. in Mechatronics and Automation (ICMA), 2014 IEEE International Conference on. 2014. IEEE.
 Zhao, L., H. Qiu, and Y. Feng, Study of Robust Filtering Application in Loosely Coupled INS/GPS System. Mathematical Problems in Engineering, 2014. 2014.
 Jorgensen, M.J., et al. IMU Calibration and Validation in a Factory, Remote on Land and at Sea. in Position, Location and Navigation Symposium-PLANS 2014, 2014 IEEE/ION. 2014. IEEE.
 Zhang, R., F. Hoflinger, and L. M. Reind, Calibration of an IMU Using 3-D Rotation Platform. Sensors Journal, IEEE, 2014. 14(6): p. 1778-1787.
 Kamiński, T., et al., Collision detection algorithms in the eCall system. Journal of KONES, 2012. 19: p. 267-274.
 Schmidt, G.T. and R.E. Phillips, INS/GPS integration architectures. 2010, DTIC Document.
 Bar-Shalom, Y., X. R. Li, and T. Kirubarajan, Estimation with applications to tracking and navigation: theory algorithms and software. 2004: John Wiley & Sons.
 Škaloud, J., Optimizing georeferencing of airborne survey systems by INS/DGPS. 1999, Citeseer.
 Chiang, K.-W., T. Duong, and J.-K. Liao, The Performance Analysis of a Real-Time Integrated INS/GPS Vehicle Navigation System with Abnormal GPS Measurement Elimination. Sensors, 2013. 13(8): p. 10599.
 Farrell, J., Aided navigation: GPS with high rate sensors. 2008: McGraw-Hill New York.
 Salychev, O. S., Inertial systems in navigation and geophysics. 1998: Bauman MSTU Press Moscow.
 Navidi, N. and R. Landry Jr, A new survey on self-tuning integrated low-cost GPS/INS vehicle navigation system in Harsh environment. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015. 40(1): p. 75.
 Navidi, N., et al., A new technique for integrating MEMS-based low-cost IMU and GPS in vehicular navigation. Journal of Sensors, 2016. 2016.