ScenarioMaker
Autonomous driving test scenario generation from real-world near-miss data
The Problem
AV validation requires billions of test miles. Simulation is fast but only tests scenarios someone thought to write. The long tail of edge cases is where the fatalities hide — and nobody writes those scenarios in advance.
Why You Win
You understand the scenario space: weather combinations, road geometry variations, traffic participant behaviors, sensor degradation modes. You know what real edges look like.
Solo Founder Path
Build a scenario generator that creates parametric variations from real-world near-miss databases. Sell to AV companies as a continuously expanding scenario library.
How AI Agents Scale It
AI agents generate, execute, and evaluate millions of scenarios autonomously, flagging novel failure modes and expanding coverage without human scenario design.
Market Background
The testing and validation segment represents a $4.5B market opportunity. AV validation requires billions of test miles. Early movers building AI-native solutions in this space can capture significant market share before incumbents adapt their legacy offerings.
Tech Stack
12-Week Roadmap
Pricing Ladder
Limited usage to evaluate the product. See what AI-powered automation looks like.
Full core features, standard integrations, email support.
All features, priority support, multi-project dashboard, API access.
SSO, on-prem option, custom integrations, dedicated support, SLA.
Competitive Landscape
Hardware-centric HIL, expensive, not AI-powered
Protocol tools, not AI test generation
General test equipment, not automotive-specific AI
YC Companies in Adjacent Space
Funded startups solving related problems — proof the market is real.
Algorithmic safety validation tools for autonomy
Generates rare and realistic failure scenarios for autonomous systems validation — directly the same as ADAS test scenario generation.
Self-driving cars
Pioneer in autonomous driving that requires massive scenario testing — the scale of ADAS testing at Cruise-level validates the need for scenario generation tools.
Transforming cities and rural areas through AV transit and technology
Autonomous shuttle deployment requires extensive scenario testing — real-world AV deployment proves the need for comprehensive test scenario generation.
Moat Analysis
Domain Knowledge advantage specific to ScenarioMaker: deep automotive expertise encoded into product logic
Data Network Effects advantage specific to ScenarioMaker: each user interaction improves the system for all users
Switching Costs advantage specific to ScenarioMaker: integration depth and workflow dependency create stickiness
Regulatory Complexity advantage specific to ScenarioMaker: constantly evolving standards require continuous domain expertise
Proof & Signals
Autonomous driving test scenario generation from real-world near-miss data. Growing market demand driven by industry transformation and AI adoption. LinkedIn posts about testing automation consistently generate high engagement in automotive engineering circles.