Projects

HARMO
Human-aware Road Mobility Optimization
Start: 10/2026
End: 09/2029

HARMO helps automated vehicles anticipate how people really drive on familiar roads. By bringing human behaviour into planning, testing and real-world pilots, the project aims to make mixed traffic safer and more predictable.
HARMO solves a critical problem in automated driving: Current systems struggle in cities because they cannot anticipate how local, "habituated" drivers behave. While most models assume drivers are always alert, real drivers on familiar routes often rely on relaxed, automatic habits. HARMO bridges this gap by delivering TRL 5 models that account for "familiarity," enabling automated vehicles to better predict and interact with the implicit, often less attentive behaviours typical of mixed traffic. HARMO aims to:
1. Model Familiarity: Quantify how repeated route exposure alters driver attention and risk acceptance.
2. Generate Human-Like Control: Develop a Neurosymbolic-Diffusion planner that combines the natural fluidity of generative AI with verifiable symbolic safety constraints.
3. Validate in Mixed Traffic: Operationalise these models in closed-loop simulations where background agents exhibit realistic learning curves.
4. Define Regulatory Criteria: Establish an "Acceptance Threshold Matrix" to certify predictability and trust.
5. Demonstrate in Real-World: Validate the framework via shadow-mode integration in urban pilots.

Building on results from i4Driving and BERTHA, HARMO treats familiarity as a dynamic latent variable. We generate a "Habituation Dataset" to calibrate HBMs, which are then integrated into a hybrid AI planning stack. This stack is validated using EU-CEM compliant scenarios, replacing static background traffic with "smart" agents. Finally, the system is benchmarked in real-world logistics and shuttle pilots, comparing HARMO’s behavioural predictions against live human traffic without altering vehicle control. By translating scientific models into "Safety Evidence Packs" for type approval, HARMO enables the design of "safe, human-like behaviour" that is predictable to other road users, supporting the trustworthy deployment of CCAM in Europe.

Role of the FZI
FZI Research Center for Information Technology undertakes key technical development tasks in HARMO and leads the project’s scientific and technical management. In T2.2, FZI works with the University of Surrey to develop the neurosymbolic planning layer as a real-time component compatible with the automated driving stack. This layer interprets perception outputs and represents traffic rules, behavioural norms, intentions and the resulting planning constraints.

FZI leads T2.3 on improving diffusion-based planning and is specifically responsible for the guidance functionality that shapes the planner’s behavioural characteristics. The planner is intended to generate more diverse and human-like trajectories, with configurable characteristics such as a sportier or more comfortable driving style and adjustable safety margins. SAFEAD is responsible for improving the planner itself. FZI also contributes to T2.5, working with SAFEAD and IRU on planner adjustments for reinforcement learning, where symbolic evaluations help align generated trajectories with normative criteria. FZI is responsible for deliverable D2.3 on diffusion-planner enhancements.

Beyond technical development, FZI leads T7.2, “Scientific and Technical Management,” throughout the project. Its responsibilities include coordinating overall strategies and schedules, monitoring work-package progress, and tracking whether deliverables are produced on time and to the expected quality. Together with the project coordination team, FZI updates the project risk matrix and supports a shared understanding of HARMO’s objectives. FZI also contributes to disseminating the project’s scientific and technical results and to their further use in research and development.

Contact person
Staff
Division: Intelligent Systems and Production Engineering
Headquarters Karlsruhe

Research focus
Applied Artificial Intelligence

AI from research to practice: We promote applied AI for business and small and medium-sized enterprises, integrating technology with law and ethics.

Mobility and Logistics

Autonomous shuttles, intelligent logistics, and software-defined vehicles: We use IT to create the connected, safe, and sustainable mobility and logistics solutions of tomorrow.

Funding notice:
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.

Project partners:
  • ENIDE Solutions
  • International Road Transport Union
  • Transport & Mobility Leuven
  • Nord University
  • Intelligent Systems for Mobility
  • Foundation Cidaut
  • University of Surrey
  • SafeAD
  • LOXO
  • Trygg Trafikk
  • Foundation CTAG
  • ITS Norway

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