Overview
What this challenge is about.
Implement LSTM, transformer, and SSM on trajectory data, compare errors and latency, write a report, earn a verifiable certificate.
The scenario
The Nagoya autonomous-vehicles research team (around 60 staff in a Japanese OEM-affiliated research center) treats architecture choice as a 2-year commitment given downstream training-pipeline coupling.
The Brief
What you'll do, and what you'll demonstrate.
Compare LSTM, transformer, and SSM for trajectory prediction with controlled compute and write the workshop-style report.
Earning criteria — what you'll demonstrate
- Implement RNN, transformer, and SSM at comparable parameter counts
- Run a fair architecture comparison under shared compute
- Evaluate sequence models with standard trajectory-prediction metrics
- Write a workshop-style architecture comparison report
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.
- Transformers
Apply transformers to solve real industry problems and demonstrate production-level capability.
- Rnn
Apply rnn to solve real industry problems and demonstrate production-level capability.
- State Space Models
Apply state space models to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Trajectory Prediction
Apply trajectory prediction to solve real industry problems and demonstrate production-level capability.
- Experiment Design
Apply experiment design 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:
Research Scientist
Comparing architecture families with proper statistical rigor is the daily work of research scientists at AV research centers and AI labs.
This challenge sharpens
- transformers
- state-space-models
- experiment-design
ML Researcher
Designing fair architecture comparisons and writing the workshop report is the applied ML-research work that bridges research and product.
This challenge sharpens
- transformers
- rnn
- trajectory-prediction
Applied AI Scientist
Architecture-choice analyses inform every applied-AI project; this challenge gives the student the methodology to lead one.
This challenge sharpens
- pytorch
- trajectory-prediction
- experiment-design