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In order to find our new features and compare the accuracy of the estimation, this study will take a further step by attempting to estimate road roughness condition from smartphones under more realistic settings, which is beyond fixed orientation and/or fastening the devices themselves tightly with vehicles while collecting data.
The observer can estimate road friction coefficient exactly in real-time and resist external disturbance.
Even in noise interference, it also can estimate road friction coefficient efficiently.
Latitude, longitude, and elevation were used to estimate road grade using a methodological approach reported elsewhere [36].
The proposed research provides a novel method to estimate road friction coefficient with strong robustness and more accurate.
Given commodity flows, it is possible to estimate road freight flows in vehicles by using a vehicle loading model.
Similar(41)
In previous studies, while there is a lot of interest in detecting road bumps and anomalies using mobile sensors, majority of them focus on identifying and locating road bump and anomalies instead of estimating road pavement condition, particularly in terms of IRI measurement and/or estimation.
As we can see from the figure, estimated road friction coefficient of front and rear wheels is very close to the real values of road friction coefficient.
The use of a combined kinematic relation plus speed scheduled Kalman filter virtual sensor for estimating road vehicle yaw rate is the topic of this paper.
Open image in new window Fig. 11 Braking torque of front and rear wheels Open image in new window Fig. 12 Simulated and estimated road friction coefficient comparison.
The current study tests the combined effects of three roadway design elements – shoulders width, guardrail existence and roadway geometry (curvature) – on objective driving measures (speed and lane position), and subjective measures (perceived safe driving speed and estimated road safety).
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