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.
The challenge is currently in a soft-open period. Registration and leaderboard access are available now so teams can begin exercising the submission workflow, validating containers, and calibrating against the current evaluation service while final data updates are prepared.
02 Registration
Registration is limited to teams affiliated with academic or industry organizations. To protect limited evaluation capacity and ensure fair participation, the team captain's verified Hugging Face primary email must be an organization address.
Registrations are reviewed to prevent duplicate or non-genuine accounts from circumventing team-level participation limits. Independent groups from the same organization are welcome: identify your lab, department, or group and briefly describe the project in the registration form.
Full challenge rules and submission limits are in the Contestant Guide.
03 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.
The current test set includes a subset of the final scenes. Additional scenes will be added later in the competition.
nuPlan Track
A lower-barrier track for teams building on the widely used nuPlan ecosystem or NAVSIM-style development workflows.
The nuPlan leaderboard is currently backed by a provisional dataset. Teams are encouraged to submit during this period to test their workflow and understand the evaluation process. When the updated nuPlan data is available, the nuPlan leaderboard will be cleared and each team's best submission will be resubmitted by the organizers.
04 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.
05 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.
06 Documentation Links
Contestant Guide
Challenge setup, tracks, scoring, and participant workflow.
Starter Kit
Baseline files and examples for building an initial submission.
Submission CLI
Command-line tooling for packaging, validating, and submitting entries.
Submission Image Requirements
Container image constraints and requirements for valid submissions.
Capability Score Computation
Reference documentation for how policy capability scores are computed.
Questions and Discussions
Use the public AlpaSim repository for bug reports, questions, and competition discussions.
07 Organizers
This competition is hosted by NVIDIA's Autonomous Vehicle Research Group, KE:SAI, and HKU.
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.
HKU
OpenDriveLab at The University of Hong Kong is a research group focused on embodied AI and autonomous driving. The lab develops open benchmarks, simulation infrastructure, world models, and end-to-end driving systems to support scalable, reproducible, and trustworthy physical AI research.