Road Classification based on System Response with Consideration of Tire Enveloping

Paper #:
  • 2018-01-0550

Published:
  • 2018-04-03
Abstract:
Road profile is the main input of vehicle system and its characteristics significantly influence vehicle performance, especially on the aspects of ride comfort, road handling, and fuel consumption. In fields of vehicle dynamics control and road network maintenance, Road observation becomes a hot topic and receives considerable attention over the last several years. To provide an easy applied yet accurate road classification method for automotive engineers, this paper presents a system response based road classifier with consideration of tire enveloping. For this purpose, tire enveloping effect is firstly modeled based on the flexible roller contact (FRC) theory, then transfer functions between road input and commonly used suspension responses i.e. sprung mass acceleration, unsprung mass acceleration and rattle space, are calculated for a quarter vehicle model. Comprehensive analysis of the influence of parameters variation, vehicle velocity, and measurement noise on transfer functions is carried out to derive the most suitable system response thereafter. Vehicle speed correction mechanism is subsequently proposed to further improve classification accuracy for complex driving conditions. Moreover, a random forest based classifier is trained by treating center spatial frequencies of the octave bands as the classifier input as per ISO-8608. Simulation results for complex road conditions with various velocities finally validate the proposed approach. The overall classification accuracy improved from F-score of 0.7547 (without velocity correction) to F-score of 0.9807 (with velocity correction). The innovations of this paper can be concluded as 1) the presented classification method is closer to reality by considering tire enveloping effect, 2) analysis of various factors and velocities correction mechanism ensures the classifier’s robustness for fickle driving conditions, 3) presented classifier only need one driving in a known road and is applicable for mass-produced commercial application compared to previous research.
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