A Simulation-First ROS 2 Platform for Autonomous Formula Student Vehicles: Mission Control, EKF State Estimation and Statistically Validated Testing

Formula Student (FS) Driverless teams must validate a perception-to-control autonomy stack long before the car's sensors and compute exist, yet published systems generally assume the hardware is already on the vehicle. This paper presents a simulation-first platform on ROS~2 Humble and the eufs\_sim2 simulator in which closed-loop controllers for all four FS Driverless events are developed, tested and statistically characterised before hardware is procured. The autonomy package depends on the simulator only through a message-definition package, so the same code is designed to run on a Jetson-based vehicle unchanged. We implement proportional speed control, pure-pursuit steering and perception-gated lap counting, integrate an Extended Kalman Filter (EKF) that fuses IMU and wheel odometry, and build a software-in-the-loop (SIL) harness with an injectable sensor-error model. A Monte-Carlo method with Wilson-score intervals shows Skidpad success of 100\% (95\% CI 67.6--100\%, $n{=}8$) up to $8^{\circ}/\sqrt{\text{min}}$ of odometry yaw drift and 87.5\% (CI 52.9--97.8\%) at $10^{\circ}/\sqrt{\text{min}}$, where the worst episode reaches 3.84~m although the median stays at 0.44~m. The SIL model predicted the full-simulator mean tracking error to within 0.01~m (0.32 vs.\ 0.33~m), and the EKF drifted 2.4\% over an 83~m run. We also document four defects in the upstream simulator, two of which fail silently. All results are from simulation; camera and LiDAR fusion are not part of the evaluated pipeline.