{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [
    "K.J. Somaiya School of Engineering, Somaiya Vidyavihar University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rishi Satish"
    },
    {
      "affs": [
        0
      ],
      "name": "Harshvardhan Singh"
    },
    {
      "affs": [
        0
      ],
      "name": "Ayon Majumdar"
    },
    {
      "affs": [
        0
      ],
      "name": "Makarand Govind Kulkarni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8k04h5xvgdjjw.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23493501",
  "fulltext_url": null,
  "id": "8k04h5xvgdjjw",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23493501",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23493501/files/A_Simulation_First_ROS_2_Platform_for_Autonomous_Formula_Student_Vehicles__Mission_Control__EKF_State_Estimation_and_Statistically_Validated_Testing.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8k04h5xvgdjjw.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:20:20Z",
      "native_id": "23493501",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23493501"
    }
  ],
  "subjects": [],
  "title": "A Simulation-First ROS 2 Platform for Autonomous Formula Student Vehicles: Mission Control, EKF State Estimation and Statistically Validated Testing",
  "title_text": "A Simulation-First ROS 2 Platform for Autonomous Formula Student Vehicles: Mission Control, EKF State Estimation and Statistically Validated Testing",
  "type": "preprint",
  "updated_at": "2026-11-03T12:20:20Z",
  "url": "https://research.draconitic.com/paper/8k04h5xvgdjjw",
  "venue": "Zenodo",
  "versions": []
}