Autonomy Solutions
Scaling Autonomy at Every Level
From Smarter Assistance to Full Independence
Smarter SAI Assistance
ototo’s SAI-powered assistance goes beyond conventional L2+, enhancing ADAS with intelligent navigation through challenging conditions.
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Enhanced Safety – Anticipates hazards and smoothly handles interactions with pedestrians, cyclists and vehicles.
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Greater Efficiency – Drives with foresight, optimizing speed, braking, and fuel consumption for a smoother, more efficient ride.
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Future-Ready – Continuously improves, enabling a seamless transition to higher autonomy levels
Boost your L2+ with the SAI intelligent assistance
FAQ
Behind the Symbols: The Foundation of Smarter Autonomy
What is Symbolic AI?
Symbolic AI is a reasoning-based approach that uses logic and structured rules to interpret situations and make decisions.
For autonomous driving, ototo’s SAI model enables full transparency and human-like judgment in complex scenarios.
Where does prediction and path planning fit into autonomy?
Prediction and path planning determine how an autonomous system anticipates events and reacts. ototo’s SAI model applies logic to how road users perceive and act, factoring in interactions and driving patterns to ensure safe, optimal navigation in dynamic environments.
Why do long-tail road scenarios matter for autonomous driving?
Long-tail scenarios make up 20% of real-world driving, where mistakes can lead to accidents. These include roadworks, complex urban intersections, and fast-changing situations requiring instant reaction. ototo’s SAI model ensures safe handling of these unpredictable cases—essential for real-world autonomy.
In which ways is ototo’s SAI Foundation Model better than a human driver?
The SAI model delivers consistent high performance, continuously analyzing the scene, predicting behaviors, and planning ahead. Unlike human drivers, who face cognitive overload and 1–2 second reaction times, ototo processes interactions instantly, adapting to new roads and traffic rules with no extra training.
How does ototo’s SAI model explain its driving decisions?
Unlike Machine Learning "black box" models, ototo’s SAI system offers full traceability. Every decision follows structured logic,
making the reasoning clear. Our simulator provides real-time logs, showing the step-by-step decision process for full transparency.
Why doesn’t ototo's Foundation Model need massive road data to scale?
Built with human-like understanding, ototo captures the logic of road behavior, recognizing patterns across different environments. Unlike machine learning-based AI that relies on location-specific datasets, ototo generalizes knowledge, enabling instant deployment with only minimal local adjustments.
Is ototo's solution compatible with any setup?
Yes, ototo is sensor-agnostic and hardware-flexible, running entirely on the vehicle without cloud reliance. It integrates seamlessly with cameras, lidar, perception, and fusion systems, requiring no HD maps or heavy computing—allowing easy deployment across vehicle platforms.
Why is ototo's solution so lightweight on hardware?
ototo uses logical reasoning instead of compute-heavy deep learning, significantly reducing processing needs. This enables high performance on minimal hardware, making it scalable and cost-effective.
How can the model make safe decisions in completely new situations?
Using generalized reasoning instead of training on data examples, ototo applies a full set of driving rules, intentions, and behaviors. Built on statistical road data, it safely handles unknown situations—whether it’s an unexpected object, new road user, or unusual road condition—reacting with confidence in real time.
Which autonomy levels can ototo support?
ototo enhances every level of autonomy where traditional systems fall short. In L2+, it improves ADAS with smarter, safer driving.
In L3, it reduces driver workload by managing complex scenarios.
In L4, it enables full autonomy, eliminating remote support and ensuring scalable, independent operation.