Road Roughness Recognition: Feature Extraction and Speed-Adaptive Classification Based on Simulation and Real-Vehicle Tests - Jie Xing; Zhun Cheng; Shuai Ye; Songwei Liu; Jiawei Lin (Machines)
Road roughness exerts a direct influence on the vertical dynamic performance of vehicles, and the accurate characterization of road roughness is essential for optimizing vehicle suspension systems. This paper addresses two key challenges in roughness recognition: feature extraction and adaptive classification under different speeds. In detail, based on simulation tests of the quarter-vehicle vertical vibration model and real-vehicle test, this paper reveals the strong correlation between the unsprung mass vertical vibration response of vehicles and road roughness. The feasibility of using unsprung mass vertical vibration response as a feature for recognizing and classifying road roughness is verified. And an adaptive road roughness classifier is proposed based on vehicle-speed-related features. Both simulation and real-vehicle results confirm that (i) the unsprung vertical vibration displacement is strongly correlated with road roughness (R2 = 0.997); (ii) road roughness can be classified with high accuracy with the unsprung mass vertical vibration response taken as the only feature (simulation tests: 98.88% to 100%; real-vehicle tests: 100%); and (iii) the accuracy of the proposed speed-adaptive classifier is 20% more accurate than the conventional classifier that does not consider vehicle speed features. This research can provide accurate road excitation for the adaptive real-time control of semi-active or active vehicle suspensions.