Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip Estimation (2405.05668v1)

Abstract: 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.

• The paper introduces LOP-UKF, a method combining LiDAR Odometry, the Pacejka model, and an Unscented Kalman Filter for precise sideslip estimation.
• It demonstrates real-time accuracy on a high-speed racing car across diverse track conditions with robust validation.
• The approach reduces reliance on expensive sensors, offering a cost-effective solution to improve vehicle stability and safety.

The paper "Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip Estimation" addresses the critical problem of estimating the vehicle lateral velocity, an essential parameter for vehicle safety and stability. This is particularly challenging because measuring the lateral component accurately often requires expensive sensors, prompting a growing interest in estimation techniques.

The authors introduce a novel methodology named LOP-UKF, which stands for LiDAR Odometry and Pacejka Model combined with an Unscented Kalman Filter (UKF). The core idea is to integrate LiDAR Odometry (LO) data with the Pacejka tire model predictions. The Pacejka model provides a mathematical representation of tire behavior, including aspects such as tire forces and moments which are difficult to capture directly. By combining this with LO, the approach aims to offer a more accurate and cost-effective solution to estimate the lateral velocity of a racecar.

Key elements of the LOP-UKF method include:

1. LiDAR Odometry: Utilizes data from LiDAR sensors to measure the vehicle's position and orientation changes over time.
2. Pacejka Tire Model: Provides theoretical estimation of the tire forces, which, when combined with LO data, can improve the overall estimation accuracy.
3. Unscented Kalman Filter (UKF): A sophisticated filtering technique that helps in fusing the data from the Pacejka model and the LiDAR odometry to produce a robust estimate of the lateral velocity.

Experimental validation was conducted using the Dallara AV-21, a high-speed racing car, tested across various circuits and track conditions. The results demonstrated that the LOP-UKF method could reliably estimate the sideslip angle, even in challenging driving scenarios where traditional methods might struggle. This validation underscores the paper’s contribution to enhancing the accuracy and reliability of vehicle dynamics estimation, especially in high-performance automotive applications.

Overall, this paper presents a significant advancement in the field of vehicle dynamics by leveraging the complementary strengths of LiDAR Odometry and the Pacejka tire model, augmented with the robust estimation capabilities of the Unscented Kalman Filter.