01 Overview
Welcome to the AlpaSim End to End Closed Loop Challenge. This competition invites teams to build autonomous driving policies and compare them head-to-head in realistic closed-loop simulation, where each policy's decisions shape the future scene it must handle.
Autonomous driving research has made major progress, but it remains hard to compare policies across labs and companies in a realistic, reproducible way. Open-loop evaluation is useful, but it misses the compounding effects that make driving hard: a small planning error can change future observations, interactions, and risk.
AlpaSim provides a shared simulator, public development data, starter tools, baseline policies, and a common containerized submission interface. Organizer-managed evaluation workers run submissions on private held-out scenarios and publish leaderboard results with both a policy capability score and a safety metric.
Across both tracks, the goal is not just to crown a winner. We want to learn which policy families are robust under distribution shift, where they fail, and how the community can make AV evaluation more trustworthy.
02 Challenge Tracks
The competition has two complementary tracks, covering both large-scale geographically diverse driving data and a lower-barrier entry point for teams working with established nuPlan-style workflows.
Physical AI AV Track
The larger-scale setting for testing whether promising policies hold up as scenario diversity and long-tail coverage increase.
nuPlan Track
A lower-barrier track for teams building on the widely used nuPlan ecosystem or NAVSIM-style development workflows.
03 Timeline
- 2026-06-15 Competition goes live Registration, public data, starter tools, and submission instructions open.
- 2026-08-15 Mid-competition checkpoint FAQ updates, leaderboard-health notes, and non-breaking clarifications.
- 2026-09-15 Rules and submission format freeze Final rules, metric implementation, Docker base image, and public submission format freeze except for critical fixes.
- 2026-10-31 Public leaderboard closes Teams select one final valid container per track. Technical reports are due.
- 2026-11-15 Final results released Final results and award decisions are released to participants.
- NeurIPS 2026 Competition track workshop Winners and selected participants present results; organizers share final analysis and lessons learned.
04 Prizes
Each track will award two NVIDIA DGX Spark prizes: one for first place and one for an innovative solution.
PAI-AV Track
Two NVIDIA DGX Spark prizes awarded.
nuPlan Track
Two NVIDIA DGX Spark prizes awarded.
05 Documentation Links
Coming soon.
06 Organizers
This competition is hosted by NVIDIA's Autonomous Vehicle Research Group and KE:SAI.
NVIDIA Autonomous Vehicle Research Group
Interdisciplinary NVIDIA Research team advancing vehicle autonomy across perception, prediction, planning, control, simulation, foundation models, and AI safety.
KE:SAI
Non-profit open-science research lab advancing robust, safe, and reproducible physical AI, with a focus on world models, autonomy, and open self-driving technology.