Filippo Ghilotti

I work on pseudo-labeling and holistic scene understanding for autonomous driving, building scalable training pipelines that turn raw multimodal data into reliable supervision. My goal is to break the labeling bottleneck, driving down cost while improving quality, through deep learning and classical optimization.

📬 Contact

e-mail
languages
Italian and English

🎓 Curriculum vitae

since 2026
Researcher at ZIB

📝 Publications and preprints

Preprints

  1. Ghilotti, F., Palladin, E., Brucker, S., Sigal, A., Bijelic, M., and Heide, F. (2026). TruckDrive: Long-Range Autonomous Highway Driving Dataset. [arXiv]
    [BibTeX]
    @misc{2026_FilippoghilottiEtAl_Truckdrivedataset_2603-02413,
      archiveprefix = {arXiv},
      eprint = {2603.02413},
      arxiv = {arXiv:2603.02413},
      primaryclass = {cs.CV},
      year = {2026},
      author = {Ghilotti, Filippo and Palladin, Edoardo and Brucker, Samuel and Sigal, Adam and Bijelic, Mario and Heide, Felix},
      title = {TruckDrive: Long-Range Autonomous Highway Driving Dataset},
      date = {2026-03-02}
    }
  2. Ghilotti, F., Brucker, S., Saidy, N., Matteucci, M., Bijelic, M., and Heide, F. (2026). UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition. [arXiv]
    [BibTeX]
    @misc{2026_FilippoghilottiEtAl_Unilipspseudolabeling_2601-05105,
      archiveprefix = {arXiv},
      eprint = {2601.05105},
      arxiv = {arXiv:2601.05105},
      primaryclass = {cs.CV},
      year = {2026},
      author = {Ghilotti, Filippo and Brucker, Samuel and Saidy, Nahku and Matteucci, Matteo and Bijelic, Mario and Heide, Felix},
      title = {UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition},
      date = {2026-01-08}
    }
  3. Feng, B., Mei, Z., Li, B., Ost, J., Ghilotti, F., Girgis, R., Majumdar, A., and Heide, F. (2025). VERDI: VLM-Embedded Reasoning for Autonomous Driving. [arXiv]
    [BibTeX]
    @misc{2025_BowenEtAl_VerdiVlmreasoning_2505-15925,
      archiveprefix = {arXiv},
      eprint = {2505.15925},
      arxiv = {arXiv:2505.15925},
      primaryclass = {cs.RO},
      year = {2025},
      author = {Feng, Bowen and Mei, Zhiting and Li, Baiang and Ost, Julian and Ghilotti, Filippo and Girgis, Roger and Majumdar, Anirudha and Heide, Felix},
      title = {VERDI: VLM-Embedded Reasoning for Autonomous Driving},
      date = {2025-05-21}
    }
  4. Palladin, E., Brucker, S., Ghilotti, F., Narayanan, P., Bijelic, M., and Heide, F. (2025). Self-Supervised Sparse Sensor Fusion for Long Range Perception. [arXiv]
    [BibTeX]
    @misc{2025_EdoardopalladinEtAl_Selfsupervisedfusion_2508-13995,
      archiveprefix = {arXiv},
      eprint = {2508.13995},
      arxiv = {arXiv:2508.13995},
      primaryclass = {cs.CV},
      year = {2025},
      author = {Palladin, Edoardo and Brucker, Samuel and Ghilotti, Filippo and Narayanan, Praveen and Bijelic, Mario and Heide, Felix},
      title = {Self-Supervised Sparse Sensor Fusion for Long Range Perception},
      date = {2025-08-19}
    }

🔬 Projects

GENAD
Generative AI for Scalable Autonomous Driving Systems

The project aims at shaping the next generation of highway autonomy for heavy trucks by combining long-range multimodal data, controllable generative models, a closed-loop virtual gym, and vision-language model (VLM) embedded reasoning. Rather than treating autonomy as a pure perception or planning problem, the project aims to advance generative simulation, reinforcement learning and chain-of-thought reasoning and language-guided representation learning as a unified research agenda.

GENAD
Mar 2026 to Mar 2029
5