Overview
What this challenge is about.
Train LSTM and transformer models on Argoverse 2 to predict urban robotaxi trajectories and evaluate displacement error. Get a verifiable certificate.
The scenario
The behavior team owns a critical-path component for the urban-only pilot; small wins in interaction-specific accuracy translate directly into fewer human-driver disengagements.
The Brief
What you'll do, and what you'll demonstrate.
Decide whether to invest a quarter of behavior-team engineering into the transformer predictor over the LSTM baseline, based on accuracy + cost evidence.
Earning criteria — what you'll demonstrate
- Implement and tune trajectory predictors on a real AV dataset
- Define and evaluate behavior-relevant interaction slices
- Trade off accuracy gains against training and inference cost
- Write a research memo a behavior team can decide on
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.
- Trajectory Prediction
Apply trajectory prediction to solve real industry problems and demonstrate production-level capability.
- Transformer Models
Apply transformer models to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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.
- Benchmarking
Apply benchmarking 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:
ML Researcher
Rigorous, slice-aware benchmarking of trajectory predictors is the daily work of a behavior-team ML researcher at any AV company.
This challenge sharpens
- trajectory-prediction
- transformer-models
- experiment-design
Research Scientist
Honest reporting of cost + accuracy trade-offs across architectures is the research-scientist discipline AV labs hire for.
This challenge sharpens
- transformer-models
- evaluation
- benchmarking
Machine Learning Engineer
Pipeline + reproducibility habits learned here transfer directly into the MLE side of behavior-prediction productionization.
This challenge sharpens
- pytorch
- benchmarking
- trajectory-prediction