Overview
What this challenge is about.
Fine-tune a 3D object detector on highway trucking data, evaluate parked vehicles near road edges, and deliver a model checkpoint for a verifiable certificate.
The scenario
The startup (around 200 staff, post-Series C) plans a 50-truck commercial pilot; any class of nuisance brake is a board-level safety + customer issue that delays the revenue ramp.
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.
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