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.

翻译：摘要：侧滑角作为影响车辆安全与稳定性的关键参数，需通过纵向与横向速度联合求解。然而，横向速度的测量通常依赖昂贵的传感器，因此常采用估算方法，这一问题已在现有文献中得到充分探讨。本文提出LOP-UKF这一新型车辆横向速度估计方法，通过融合LiDAR里程计与Pacejka轮胎模型预测值，并利用无迹卡尔曼滤波（UKF）实现鲁棒估计。该方法为传统技术路线提供了显著替代方案，在极端工况下仍能保持可靠性。我们展示了使用Dallara AV-21赛车在不同赛道和路面条件下获得的实验结果，验证了该方法的有效性。