Abstract

Determining the mass of a vehicle from ground based passive sensor data is important for many security and traffic safety reasons. A vehicle consists of multiple dependent and independent systems that each respond differently to changes in vehicle mass. In some cases, the responses of these vehicle systems can be measured remotely. If these remotely sensed system responses are correlated to the vehicle's mass, and the required vehicle parameters were known, it would be possible to calculate the mass of the vehicle as a function of these responses.

The research described here investigates multiple vehicle phenomenologies and their correlation to vehicle load. Brake temperature, engine acoustics, exhaust output, tire temperature, tire deformation, vehicle induced ground vibration, suspension response, and engine torque induced frame twist were all evaluated and assessed as potential methods of remotely measuring a vehicle's mass. Extensive field experiments were designed and carried out using multiple sensors of various types; including microphones, accelerometers, high-speed video cameras, high-resolution video cameras, LiDAR, and thermal imagers. These experiments were executed at multiple locations and employed passenger vehicles, and commercial trucks with loads ranging from zero to beyond the recommended load capacity of the vehicle. The results of these experiments were used to determine if the signature for each phenomenology could be accurately observed remotely, and if so, how well they correlated to vehicle mass. The suspension response and engine torque induced frame twist phenomenologies were found to have the best correlation to vehicle mass of the phenomenologies considered, with correlation values of 90.5% and 97.7%, respectively. Physics-based models were built for both the suspension response, and the engine torque induced frame twist phenomenologies. These models detailed the relationship between each phenomenology and the mass of the vehicle. Full-scale field testing was done using improved remote detection methods, and the results were used to validate the physics-based models. The results of the full-scale field testing showed that both phenomenology could accurately calculate the mass of the vehicle remotely, given that certain vehicle parameters were accurately known. The engine torque induced frame twist phenomenology was able to find the mass of the test vehicle to within 10% of the true mass. Using the suspension response phenomenology the mass was accurately predicted as a function of its location on the vehicle. For either phenomenology to be effective, certain vehicle parameters must be known accurately; specifically the spring constant and damping coefficients of the vehicle's suspension, the unloaded mass, the unloaded center of gravity, and the unloaded moment of inertia of the vehicle. The models were also used to propagate measurement and parameter uncertainty through the vehicle mass calculation to arrive at the uncertainty in the mass estimation. Finally, the results of both the phenomenologies were combined into a single vehicle mass estimate with a smaller uncertainty than the individual vehicle system estimations taken alone.

Library of Congress Subject Headings

Loads (Mechanics)--Remote sensing; Motor vehicles--Remote sensing

Publication Date

8-5-2016

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Carl Salvaggio

Advisor/Committee Member

Edward Hensel

Advisor/Committee Member

Alfred Garrett

Comments

Physical copy available from RIT's Wallace Library at TA654.3 .M35 2016

Campus

RIT – Main Campus

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