Build eVTOL Mobility Risk Monitor with Google ADK and Aider
Urban air mobility routes require real-time atmospheric collision and safety constraint monitoring. Build an AI flight monitoring agent using Google ADK with Gemini Multimodal capabilities and refactor code pipelines using Aider to guarantee sub-100ms risk detection on flight telemetry.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an aerial safety analysis agent with Google Agent Development Kit and automated code refactoring using Aider.
How work is evaluated
Evaluates dynamic flight risk identification and system optimization metrics.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
latency_threshold
Ensures flight risk processing completes in under 100ms
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
risk_detection_recall
Recall metric on hazardous flight corridor events • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Develop multimodal flight corridor risk analyzers using Google Agent Development Kit (ADK)
Use Aider AI engineering tooling to automatically refactor vector processing routines for low latency
Assess dynamic aerial traffic conflicts and weather constraints using Gemini vision models
Reference links and supporting material
Simulated urban eVTOL radar frames and atmospheric weather telemetry vectors.
How this agent runs
Evaluates dynamic flight risk identification and system optimization metrics.
Challenge input
JSON containing drone altitude, speed, wind vector, and camera snapshot URI
Google ADK
Provides Gemini multimodal agent integration
Aider
Automates local code refactoring for performance optimization
Evaluated output
JSON containing risk level, action, and latency ms
- Ensures flight risk processing completes in under 100ms
- Recall metric on hazardous flight corridor events • target: 0.98 • range: 0-1
- Benchmark: eVTOL-Safety-Bench
- Risk Detection Recall target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Aider · Required
- Google ADK · Optional
- Google · Optional
- Aider · Required
Evaluation contract
- latency_threshold · Weight 1
- risk_detection_recall · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.