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

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.

er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars. This paper presents the complete software architecture used by team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modelling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed respectively second and third.