NAVSIM-v-2

NAVSIM-v-2 is a dataset/benchmark designed to evaluate end-to-end autonomous driving systems by providing diverse scenarios for trajectory planning and decision-making.

Papers using NAVSIM-v-2 (7)

DriveStack-VLA: Render-Teacher Alignment for BEV-Based DeepStack Vision-Language-Action Model2026LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving2026DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving2026HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving2026DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving2026MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving2025IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model2025