6 papers
Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos
Anurag Ghosh, Francesco Pittaluga, Khiem Vuong +4
Self-driving simulations typically rely on data collected in a small number of cities or on hand-authored synthetic scenarios. Dashcam videos cover a far broader range of locations…
LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents
Yun He, Francesco Pittaluga, Ziyu Jiang +3
LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D…
HorizonWeaver: Generalizable Multi-Level Semantic Editing for Driving Scenes
Mauricio Soroco, Francesco Pittaluga, Zaid Tasneem +5
Ensuring safety in autonomous driving requires scalable generation of realistic, controllable driving scenes beyond what real-world testing provides. Yet existing instruction guide…
RAD-LAD: Rule and Language Grounded Autonomous Driving in Real-Time
Anurag Ghosh, Srinivasa Narasimhan, Manmohan Chandraker +1
We present LAD, a real-time language--action planner with an interruptible architecture that produces a motion plan in a single forward pass (~20 Hz) or generates textual reasoning…
HorizonForge: Driving Scene Editing with Any Trajectories and Any Vehicles
Yifan Wang, Francesco Pittaluga, Zaid Tasneem +3
Controllable driving scene generation is critical for realistic and scalable autonomous driving simulation, yet existing approaches struggle to jointly achieve photorealism and pre…
LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation
Wei-Jer Chang, Wei Zhan, Masayoshi Tomizuka +2
Evaluating autonomous vehicles with controllability enables scalable testing in counterfactual or structured settings, enhancing both efficiency and safety. We introduce LangTraj,…