Intelligent Forecasting Using Dead Reckoning With Dynamic Errors

Barrios, Cesar

doi:10.1109/TII.2015.2514086

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Intelligent Forecasting Using Dead Reckoning With Dynamic Errors

2016-12-01

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Barrios, Cesar *

Yuichi Motai

Dryver R. Huston

DOI：10.1109/TII.2015.2514086

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Abstract

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A method for integrating, processing, and analyzing sensing data from vehicle-mounted sensors for intelligent forecasting and decision-making is introduced. This dead reckoning with dynamic errors (DRWDEs) is for a large-scale integration of distributed resources and sensing data intervehicle collision avoidance system. This sensor fusion algorithm is introduced to predict the future trajectory of a vehicle. Current systems that predict a vehicle's future trajectory, necessary in a network of collision avoidance systems, tend to have a lot of errors when the vehicles are moving in a nonstraight path. Our system has been designed with the objective of improving the estimations during curves. To evaluate this system, our research uses a Garmin 16HVS GPS sensor, an AutoEnginuity OBDII ScanTool, and a Crossbow three-axis accelerometer. Using Kalman filters (KFs), a dynamic noise covariance matrix merged together with an interacting multiple models (IMMs) system, our DRWDE produces the future position estimation of where the vehicle will be 3 s later in time. The ability to handle the change in noise, depending on unavailable sensor measurements, permits a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared with a more common KF implementation that runs at the rate of its slowest sensor (1 Hz in our setup), our experimental results showed that our DRWDE (running at 10 Hz) yielded more accurate predictions (25%-50% improvement) during abrupt changes in the heading of the vehicle.

Keywords:

Collision avoidance

course correction

dead reckoning

global positioning system

Kalman filters

road vehicles

sensor fusion

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