LOP-UKFによるドリフト推定：リアルタイムレースカー横滑り角推定のためのLiDARオドメトリとPacejkaモデル
Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip Estimation
LiDARオドメトリとPacejkaタイヤモデルの予測をUKFで統合し、高価なセンサなしで車両の横滑り角をリアルタイム推定する手法を提案。実車実験で有効性を検証した。
著者: Alessandro Toschi, Nicola Musiu, Francesco Gatti, Ayoub Raji, Francesco Amerotti, Micaela Verucchi, Marko Bertogna
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
The sideslip angle, crucial for vehicle safety and stability, is determined using both longitudinal and lateral velocities. However, measuring the lateral component often necessitates costly sensors, leading to its common estimation, a topic thoroughly explored in existing literature. This paper introduces LOP-UKF, a novel method for estimating vehicle lateral velocity by integrating Lidar Odometry with the Pacejka tire model predictions, resulting in a robust estimation via an Unscendent Kalman Filter (UKF). This combination represents a distinct alternative to more traditional methodologies, resulting in a reliable solution also in edge cases. We present experimental results obtained using the Dallara AV-21 across diverse circuits and track conditions, demonstrating the effectiveness of our method.
