Overview
What this challenge is about.
Lane-Change Intent Classifier from Dashcam Video. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockchai...
The Brief
What you'll do, and what you'll demonstrate.
Train a temporal vision model that classifies neighboring-vehicle lane-change intent from dashcam video and characterize its real-world failure modes for the perception team.
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
- Apply temporal vision models (VideoMAE, TimeSformer, or CNN+GRU) to a short-clip classification task
- Use a cross-city train/test split to measure real-world generalization
- Profile a model for on-device latency and reason about deployment trade-offs
- Communicate a model's failure modes honestly to a downstream consumer team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Visual Intelligence and Visual Reasoning
Master · Computer Vision
Strong alignment
This challenge maps to Visual Intelligence and Visual Reasoning 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.
- Video Understanding
Apply video understanding to solve real industry problems and demonstrate production-level capability.
- Temporal Modeling
Apply temporal modeling to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Generalization
Apply generalization to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Perception
Apply perception 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
Shipping a temporal vision model with city-split evaluation and a deployment-aware latency profile is exactly the day-one task of a CV engineer on a perception team.
This challenge sharpens
- video-understanding
- temporal-modeling
- perception
Machine Learning Engineer
Training a model at two sizes, profiling latency, and writing an integration note for a consumer team mirrors MLE work in any production CV stack.
This challenge sharpens
- model-evaluation
- pytorch
- temporal-modeling
Applied AI Scientist
Building a defensible generalization story (cross-city split, sliced failure modes) and translating it into a threshold recommendation is the texture of applied-AI-scientist work in autonomous vehicles.
This challenge sharpens
- generalization
- model-evaluation
- video-understanding