Relative Roughness Measurement Based Real-Time Speed Planning for Autonomous Vehicles on Rugged Road
Liang Wang, Tianwei Niu, Shoukun Wang, Shuai Wang, Junzheng Wang
Abstract
In order to guarantee autonomous vehicles' autonomy, mobility, and ride quality in rugged environments, a real-time speed planning method based on the time-frequency transformation of terrain characteristics is designed to achieve adaptive speed planning of autonomous vehicles in rough ground. On the one hand, the vertical profile of the lidar's point cloud data is converted from the time domain to the frequency domain in real time, and the integrated area of the sub-frequency range in the frequency domain is chosen as the relative roughness quantification value to realize the roughness quantification under various terrains. On the other hand, to model the relationship between vehicle speed and relative roughness, iterative search is utilized to create a speed and roughness model, and sliding windows are employed to update the roughness to achieve continuous mapping between speed and roughness. Ultimately, a number of tests were conducted on various rough roads using the oil exploration vehicle EV-56 as the study object. The experimental results show that the proposed method can identify the terrain roughness changes under complex terrain and change their speed within 0.2 m accuracy.
BibTeX
@inproceedings{iros2023_relativeroughnes,
title = {Relative Roughness Measurement Based Real-Time Speed Planning for Autonomous Vehicles on Rugged Road},
author = {Liang Wang and Tianwei Niu and Shoukun Wang and Shuai Wang and Junzheng Wang},
booktitle = {IROS 2023},
year = {2023}
}