Overview
What this challenge is about.
Train a 3D Object Detector for Highway Trucking. Expert-level challenge in code. Writing production code that solves real engineering problems, earn a blockc...
The Brief
What you'll do, and what you'll demonstrate.
Fine-tune a 3D detector so the shoulder-vehicle slice mAP improves by at least 5 points without regressing other categories.
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
- Fine-tune a state-of-the-art 3D detector on a public AV dataset
- Define and evaluate operationally-relevant evaluation slices
- Diagnose regression risk across categories
- Communicate perception trade-offs to engineering leadership
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI for Autonomous Vehicles
Master · Applied Ai
Strong alignment
This challenge maps to AI for Autonomous Vehicles at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- 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.
- Slice Evaluation
Apply slice evaluation to solve real industry problems and demonstrate production-level capability.
- Transfer Learning
Apply transfer learning 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
Fine-tuning 3D detectors with slice-aware evaluation is the day-one perception work at any AV company.
This challenge sharpens
- 3d-object-detection
- perception
- transfer-learning
Machine Learning Engineer
Reproducible training pipelines and rigorous regression checks are the MLE habits that get safety-critical models into production.
This challenge sharpens
- ml-pipelines
- pytorch
- slice-evaluation
AI Safety Researcher
Operationally-defined evaluation slices for failure modes is the safety discipline AV safety researchers practice every quarter.
This challenge sharpens
- slice-evaluation
- perception
- 3d-object-detection