Overview
What this challenge is about.
Multi-Sensor Late-Fusion Prototype for an Indoor AGV. Advanced challenge in code. Writing production code that solves real engineering problems, earn a block...
The Brief
What you'll do, and what you'll demonstrate.
Recommend between early- and late-fusion sensor architectures for indoor static-obstacle detection on accuracy, latency, and maintainability.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Implement two distinct sensor-fusion architectures end-to-end
- Evaluate detection performance with per-class and latency metrics
- Diagnose fusion-specific failure modes
- Recommend a perception architecture with trade-offs spelled out
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Sensor Fusion
Apply sensor fusion to solve real industry problems and demonstrate production-level capability.
- 3d Object Detection
Apply 3d object detection to solve real industry problems and demonstrate production-level capability.
- Perception
Apply perception to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Ml Pipelines
Apply ml pipelines to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Computer Vision Engineer
Sensor-fusion architecture bake-offs are core CV-engineer work at every AGV, drone, and AV company.
This challenge sharpens
- sensor-fusion
- 3d-object-detection
- perception
Machine Learning Engineer
Disciplined comparison with per-class metrics + latency reporting is the MLE habit production teams expect.
This challenge sharpens
- pytorch
- benchmarking
- ml-pipelines
Applied AI Scientist
Translating fusion comparison into a maintainability-aware recommendation is the applied AI scientist's daily output.
This challenge sharpens
- sensor-fusion
- benchmarking
- perception