NavSafe- infinity: Benchmarking Closed-Loop Driving Safety in Photorealistic Environments

arXiv:2609.26618 · cs.RO · Submitted 2026-09-22 · Read on arXiv

cs.RO

Submitted: 2026-09-22

Updated: 2026-10-04

Code: https://github.com/NVlabs/alpasim

Project page: https://navsafe-vail.github.io

Terminology

• Benchmark — 23× in this paper · explained in 2 episodes
• Actor — 6× in this paper · explained in 3 episodes
• Reward Hacking — 6× in this paper · explained in 8 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Traffic Crashes — 4× in this paper
• Traffic Incidents — 4× in this paper
• Traffic Violations — 4× in this paper
• 3D Gaussian Splatting — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Gaussian Splatting — 3× in this paper · explained in 4 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Vulnerable Road User Crashes — 3× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Driving Score — 2× in this paper
• Event Progress — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• World Model — 2× in this paper · explained in 7 episodes
• 3D Gaussian Splatting (3DGS) — 1× in this paper · explained in 4 episodes
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Driving Efficiency — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Imitation Learning (IL) — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Success Rate — 1× in this paper
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• RAP: 3D Rasterization Augmented End-to-End Planning
• nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving
• WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
• DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
• NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
• SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving
• SimScale: Learning to Drive via Real-World Simulation at Scale
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
• DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
• DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer
• SimWAM: A Simple World Action Model for End-to-End Autonomous Driving

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving