Tempero Scenario Intelligence | Closing the Loop in Automated-Driving Validation
Scenario Intelligence · Regulation-aware · Autonomous

Tempero Scenario Intelligence closes the loop between scenario authoring, large-scale fuzzing, and engineering insight — autonomously.

From a single plain-language brief, the platform synthesises test scenarios together with the formal properties to be measured against them, drives optimization-guided exploration across millions of parameter combinations, and reasons about its own findings — converging on the regions where an automated-driving system is genuinely vulnerable, and returning not just the failing scenarios but a map of the design space they came from.

How the loop works Industrial validation
94%
Compute & license cost reduction
One brief
Plain English in, scenarios and formal properties out
Two outputs
The scenarios — and the map of the space
Why now

The more we delegate design to AI, the less we understand what we built.

Generative AI is taking over scenario formulation itself. The engineer no longer specifies every candidate or every parameter — they ask for coverage of an operating domain, for corner cases, for stress on a controller. That is powerful, but it separates the engineer from the artifact by layers of automation. A tool that generates a scene and stops returns results without understanding — and when the system under test is itself an opaque learned policy with no code to audit, neither the scenario space nor the system is human-readable. Closing that gap is not a refinement. It is the only way an engineer can know what they have actually built.

01

The billion-scenario space

Certification for higher autonomy implies scenario spaces of over a billion concrete tests. Exhaustive simulation is not merely expensive — it is structurally impossible. No engineer can mentally traverse it, and no fixed grid enumerates what matters.

02

Every update resets the clock

Each software update — classical or neural — resets the verification clock and its compute bill. Software-defined vehicles evolve at software pace, so continuous re-verification is the baseline, not a one-off milestone.

03

Evolving rules, auditable evidence

Safety regulation for automated driving is still moving — and moving toward auditable, continuous, evidence-driven assessment. A defensible workflow must attribute findings to requirements as those requirements change.

The closed loop

A scenario is not a fixture to check. It is a variable to explore.

Authoring, searching, and understanding are one continuous process. Each pass produces a better-aimed scenario than the last — and the return path is what generate-and-stop tools never build.

01 · SYNTHESISE

Brief → scenario

A plain-language ask becomes a valid, parameterised scenario in the open standard formats, together with the formal property to be measured against it.

02 · ILLUMINATE

Search the space

Optimization-guided exploration maps where behaviour holds and where it breaks — returning the frontier, not a single verdict.

03 · REASON

Evidence & implication

Each run is read as a designed experiment: statistical evidence, the requirement it implicates, and the refinement that should follow.

↻   Then the loop returns. An LLM hands you a scene and stops; simulating by hand afterwards is a step backwards. Tempero is what continues past generation — searching, explaining, refining, and running again.
01 · Synthesise

A creativity tool for scenarios that are hard to write by hand.

Authoring a scenario in the open standard formats is difficult. Authoring one that is meaningful — that genuinely exercises the property you care about — is harder still, and it is where most effort is lost before testing even begins.

Tempero turns a plain-language brief into a valid, parameterised scenario — and, alongside it, expresses the property to be measured on the ego trajectory in precise, formal terms. The two are distinct objects: a scenario stands on its own; a property is what validation and search evaluate against it. Getting them to align is the craft, and it is what makes the rest of the loop possible — without a precise statement of what must hold, there is nothing to search against and nothing to attribute a finding to.

This is where generation-only tools stop. It is where Tempero begins.

# brief
“a lead vehicle pulls away to reveal
 stopped traffic — ego must not collide”

scenario reveal_after_cutout {
  actors  ego, lead, blocking ×2
  params  speed, gap, lateral_rate, …
  property always( reveal →
          eventually[0,T] min_distance > 0 )
}
02 · Design-space illumination

Not a single worst case — the frontier where behaviour breaks.

Classical optimization returns one optimum and discards everything else. Illumination — the quality-diversity paradigm — returns a structured archive: the most demanding case for every region of the space, and with it the limits themselves — beyond this speed, below this gap, past this curvature.

What comes back is not a scenario. It is a map: the critical regions, the gaps never explored, the representative extremes, and the parameters that genuinely matter. Because the system under test is often an opaque learned policy, this is how you use AI to make AI legible — the machine performs the exploration no engineer could, and the engineer keeps the judgement.

feature 1feature 2criticality
benigndemandingcriticalelite — hardest case in its region

Illustrative: performance surface over a two-dimensional feature space

03 · Reasoning

Each run is a designed experiment, not a batch of results.

A meta-reasoning layer reads every completed run: it extracts the statistical evidence, ties each finding to the requirement it implicates, and proposes the next iteration of refinements — converging the search on genuine vulnerability rather than restating what was already known. Every proposal is provenance-tracked, and where statistical and regulatory guidance disagree, the conflict is surfaced rather than silently resolved.

That is one powerful way to use it. The same loop is equally an open-ended instrument: begin from a rough idea and refine a scenario until it surfaces the edge cases, near-misses, and limits nobody thought to look for.

①  Agreement

The worst case found sits inside the requirement’s must-avoid envelope — a genuine violation, not a low-signal artefact.

②  Mismatch

The search ranks one parameter dominant, but the requirement names a different driver — inert here only because part of its range was never sampled.

⚠  Conflict — surfaced, not auto-resolved

A statistical refinement would prune a case the requirement names as limiting. Both are shown; the engineer decides.

The principle behind the platform

Every autonomous scenario system should return two things.

01  Artifacts

The generated scenarios — valid, executable, and paired with a property specified precisely enough to measure.

02  Knowledge

An explicit representation of the space explored: its coverage, its structure, its boundaries, and the regions where behaviour turns critical.

Generate-and-stop delivers only the first. The more autonomy we hand to AI in design, the more the second becomes mandatory — and it is what Tempero returns by construction.

Industrial Validation

Benchmarked against official
regulatory certification standards

Not simulated benchmarks — validated against the actual test suites used for regulatory approval.

Automated lane-keeping — official certification suite

Efficiency in ADAS/AD Verification

Addressing the “needle in a haystack” challenge of higher-autonomy certification. Tempero’s verification suite identified the only 2 critical safety cases out of over 40,000 valid scenarios — using a fraction of traditional computational resources.

94%
Compute & license
cost reduction
2,434
Runs vs 40,000+
traditional
0.005%
Critical scenarios
in full space
Verification Coverage Analysis

1M+ Scenarios

Generated to explore the complete configuration space of the certification scenario

40,000+ Valid

Simulation scenarios identified within regulatory constraints

AEB Urban Risk Assessment

Fine-Grained Risk Assessment of AEB in Urban Settings

Tempero conducted a detailed assessment of Automated Emergency Braking in two urban settings with varying traffic densities — demonstrating context-specific safety evaluation that global statistics cannot provide.

~12k
Optimization-driven
virtual simulations
2
Urban contexts
compared

Setting td1 — AEB effective

Setting td1

Setting td2 — AEB limited

Setting td2

Context-Specific Insight

Unlike global statistics — results are tied to specific traffic and environmental conditions.

V2I / V2V Ready

Assessment accounts for new mobility contexts including connected infrastructure

Fits the toolchain already in place

An engine that rides on your stack — not a stack to migrate to.

Standards-native, simulator- and compute-agnostic, and steerable in plain English through a standard agent interface.

Plain-language surface

The whole loop is driven conversationally through a standard agent-integration protocol — from a development environment, a chat surface, or a CI step.

Open-standard native

Consumes and emits the open scenario and road-network formats. No proprietary scenario language to adopt or migrate to.

Simulator-agnostic core

A thin adapter over the real simulation tool rather than a reimplementation — it rides on the execution layer already trusted in the pipeline.

Compute-agnostic

A short interactive loop locally, or a full search on-premise or in the cloud — sized to the problem, and at home inside CI/CD.

About Tempero

Founded on a conviction

Tempero (Latin: “to temper” or “refrain”) reflects our core mission: to mitigate the risks of high-risk AI systems. We identified a structural gap in the AI landscape long before the EU AI Act was finalized — a gap between academic legal frameworks and the rigorous technical proof required to ensure cyber-physical safety.

Our founding team brings deep R&D backgrounds in Automated Reasoning, Formal Testing, and Constraint Solving, with previous roles at Microsoft Research and leading autonomous systems institutes. We didn’t just build a tool for cars; we built a framework to translate high-level regulatory theory into executable safety evidence for AI decision-making in the physical world.

While our vision is broad, our execution is strategic. We prioritize Automotive SDVs today to leverage mature standards like OpenSCENARIO and meet the urgent global demand for scalable, certified safety.

Tempero Ecosystem
Our Team
Youssef Hamadi

Youssef Hamadi

Co-Founder

Sathia Subbarayan

Sathia Subbarayan

Co-Founder

Bala Murali

Bala Murali

Engineer

Santhoshi K S

Santhoshi K S

Engineer

Scenario intelligence, end to end

From a plain-language brief to a map of where your system breaks.

Synthesis, search, and reasoning in one closed loop — returning the scenarios and the understanding behind them.

tempero.tech