Navsim

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navsim is an open-source AI tool for autonomous driving simulation and benchmarking. It offers data-driven, non-reactive autonomous vehicle simulation, combining efficiency with robustness for evaluating AV systems.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is navsim?

navsim is an open-source platform designed for autonomous driving simulation and benchmarking. It introduces Pseudo-Simulation, a novel evaluation methodology that merges the efficiency of open-loop evaluation with the robustness of closed-loop evaluation. By augmenting real data with synthetic observations, navsim achieves strong correlation with traditional closed-loop simulations while significantly reducing computational resources. This tool is ideal for researchers and developers in the autonomous driving field, providing a faster and more scalable approach to validate AV algorithms and behaviors. It supports data-driven, non-reactive autonomous vehicle simulation and benchmarking, making it a valuable resource for large-scale, rapid validation.

Best used for

Ideal for developers and data scientists who need to efficiently simulate autonomous driving scenarios, benchmark AV algorithms, and validate autonomous vehicle behaviors. Especially valuable for researchers requiring a scalable and robust evaluation framework that bridges the gap between open-loop and closed-loop simulations.

Common actions

simulate autonomous vehicles

benchmark AV algorithms

evaluate driving systems

validate AV models

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Capabilities

Key features

Pseudo-simulation methodology
Data-driven AV simulation
Non-reactive vehicle simulation
Benchmarking leaderboards
Metric caching
Traffic agent policies

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is Pseudo-Simulation in navsim?

Pseudo-Simulation is a novel AV evaluation method introduced by navsim. It combines the efficiency of open-loop evaluation with the robustness of closed-loop evaluation by augmenting real data with synthetic observations near the planned trajectory. This approach achieves strong correlation with traditional closed-loop simulations while being significantly faster and easier to scale.

How does navsim compare to traditional closed-loop simulations?

navsim's pseudo-simulation achieves a strong correlation with traditional, computationally expensive closed-loop simulations but requires approximately 6x less compute. Unlike traditional methods, it is neither sequential nor interactive, allowing for open-loop computation of all evaluation metrics, which enhances speed and scalability.

Can navsim be used for autonomous driving challenges?

Yes, navsim serves as the primary evaluation framework for challenges like the AGC2025 NAVSIM End-to-End Driving Challenge. It provides leaderboards and specific datasets for participants to submit and benchmark their autonomous driving models, with clear guidelines on allowed training data and submission processes.

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