The World Model for Autonomous Driving
ototo develops a Neurosymbolic World Model
that combines physical and behavioral understanding
to create a persistent, explainable, and fully configurable
representation of the real world.
Designed for the Physical AI ecosystem
World Model platforms
Consistent world evolution
Create driving worlds that remain physically and behaviorally correct over unlimited time horizons.
True digital twins
Generate realistic multi-agent interactions with rich semantic ground truth for training, testing and validation.
Unlimited scenario generation
Automatically create scenarios of any complexity - from dense urban traffic to emergencies, roadworks, illegal behavior, and accidents.
Closed-loop validation
Simulate realistic scene evolution for every ego-vehicle action, enabling rapid iteration and bridging the gap between simulation and real-world driving.
Autonomous driving
L2++
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Hazard anticipation
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Traffic-aware path planning
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Smooth lane-keeping and merges
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Real-time reasoning
L3
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ODD extension
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Transparent decision-making
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Highway merging & lane changes
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AI-human collaboration
L4
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Full urban and highway autonomy
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Dynamic path planning
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Multi-agent interactions
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Scalable fleet automation
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OEMs
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Robotaxis
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Tier 1 suppliers
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AD software
Built for real-world deployment
Trusted
Full transparency: The Neurosymbolic AI model's real-time output explains its driving decisions, preserving privacy while meeting safety and regulatory trust requirements.
Lean design
The Neurosymbolic AI system runs on any hardware, with minimal sensors required and optionally without mapping.
It is compact, in-vehicle, enabling full autonomous driving.
ODD scaling
The Neurosymbolic AI system's architecture has a universal driving foundation layer, a localized application layer and integrated domain expertise, enables local driving capabilities and rapid onboarding of new locations.
Urban ready
Mastering urban chaos: heavy traffic merges, drop-offs, complex intersections. The system handles it all with contextual prediction, accurate planning and control.
Neurosymbolic AI is the solution
Physical AI requires more than just data - it requires real understanding. Neurosymbolic AI mirrors the way humans reason, using logic and context to anticipate behavior, explain decisions and adapt in real time.
Unlike Machine Learning systems, it can handle complex long tail scenarios and provide transparency and efficiency, bringing safe and scalable autonomy within reach.
Designed for the long tail
Long tail scenarios are the toughest challenge in autonomous driving.
While each one may be uncommon, together they account for a significant share of real-world driving and often limit safety and scalability.
These include edge cases like roadworks, complex urban intersections, and fast-changing events that demand instant response. The Neurosymbolic AI model’s deep understanding of the road enables it to handle long-tail scenarios with precision.​
About ototo
ototo’s story began in 2017, when we set out to develop a Neurosymbolic AI Foundation Model capable of tackling real-world challenges. What started as an ambitious R&D project quickly revealed its potential to solve one of autonomous driving’s toughest problems: the long-tail road scenarios.
In 2023, we founded ototo to bring this breakthrough to life. Our vision gained strong industry traction and deep-tech VC funding, validating our approach.
In 2024, we launched the first fully operational version of our technology and expanded globally with offices in Europe and Japan.
