Overview
What this challenge is about.
You fuse LiDAR and camera detections for an autonomous yard truck, then measure pedestrian recall gains. You get a verifiable certificate.
The scenario
The scale-up (around 80 staff, 3 ports served) cannot ship to a fourth port until pedestrian recall under direct sunlight crosses 99 percent; the current LiDAR-only stack sits near 96 percent.
The Brief
What you'll do, and what you'll demonstrate.
Lift pedestrian recall under bright-sun conditions via a late-fusion module without hurting precision elsewhere.
Earning criteria — what you'll demonstrate
- Apply late-fusion strategies to combine LiDAR and camera detections
- Evaluate detection improvements at fixed precision
- Slice metrics by environmental condition to expose real-world gains
- Translate a prototype into a production-ready integration spec
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.
- Lidar Perception
Apply lidar perception to solve real industry problems and demonstrate production-level capability.
- Object Detection
Apply object detection to solve real industry problems and demonstrate production-level capability.
- 3d Perception
Apply 3d perception to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Computer Vision Engineer
LiDAR-camera fusion with sliced evaluation under real environmental conditions is core CV-engineer work at any autonomous-vehicle or yard-automation company.
This challenge sharpens
- sensor-fusion
- lidar-perception
- object-detection
Machine Learning Engineer
Shipping a prototype with an integration spec for a production engineer mirrors how MLEs hand off perception components in robotics teams.
This challenge sharpens
- python
- evaluation
- 3d-perception
Applied AI Scientist
Designing and ablating a fusion strategy against a fixed-precision target is the applied-AI scientist's bread and butter on perception teams.
This challenge sharpens
- sensor-fusion
- evaluation
- 3d-perception