NuPlan: a new closed-loop planning test for autonomous driving
Meet NuPlan — a fresh way to check how self-driving cars think ahead, not just guess next moves.
Unlike older tests that look only at short predictions, NuPlan uses a closed-loop setup so cars must react over time and keep planning.
The project offers a big dataset — about 1500 hours of real driving from Boston, Pittsburgh, Las Vegas and Singapore — with very different traffic and road habits, it helps systems learn from real human driving.
There is also a lightweight simulator to replay scenes and test choices again and again, so planners get tried in many situations.
The idea is simple: score how safe, smooth and useful a plan really is, not only how close it looks on paper.
NuPlan aims to make testing fair and fast, invites teams to submit ideas and compete to make better planning for real streets.
Watch for the dataset and challenges coming soon, they should speed up progress.
Read article comprehensive review in Paperium.net:
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
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