[2506.04218] Pseudo-Simulation for Autonomous Driving

Computer Science > Robotics arXiv:2506.04218 (cs) [Submitted on 4 Jun 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:Pseudo-Simulation for Autonomous Driving Authors:Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang, Yakov Miron, Marco Aiello, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta View a PDF of the paper titled Pseudo-Simulation for Autonomous Driving, by Wei Cao and 13 other authors View PDF HTML (experimental) Abstract:Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, while being efficient and data-driven, relies on metrics that generally overlook compounding errors. In this paper, we propose pseudo-simulation, a novel paradigm that addresses these limitations. Pseudo-simulation operates on real datasets, similar to open-loop evaluation, but augments them with synthetic observations generated prior to evaluation using 3D Gaussian Splatting. Our key idea is to approximate potential future states the AV might encounter by generating a diverse set of observations that vary in position, heading, and speed. Our method then assigns a higher importance to synthetic observations that best match the AV's likely behavior using a novel proxim...