Autonomous Driving Simulation

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Latest papers 217

25 papers in the last four weeks, up 79% on the four weeks before. 0.1% of all new papers.

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Dynamics Distillation for Efficient and Transferable Control Learning
May 2, 2026Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian +2Vehicle Dynamics Transfer Learning
University of Toronto · Vector Institute · NVIDIA +1
GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment
Apr 30, 2026Ziang Guo, Chen Min, Xuefeng Zhang +5End-to-End Autonomous Driving RL for Autonomous Driving
Intelligent Space Robotics Laboratory, Skolkovo Institute of Science and Technology, Moscow, Russia. · Research Center for Intelligent Computing Systems, SKLP, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. · SuZhou Automotive Research Institute, Tsinghua University, China. +1
Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA
Apr 30, 2026Feeza Khan Khanzada, Jaerock KwonDomain Generalization Cross-Domain Generalization
Department of Electrical and Computer Engineering, University of Michigan–Dearborn, Dearborn, MI 48128 USA
Judge, Then Drive: A Critic-Centric Vision Language Action Framework for Autonomous Driving
Apr 30, 2026Lijin Yang, Jianing Huang, Zhongzhan Huang +2VLMs for Autonomous Driving Vision-Language-Action Models
Bosch Research
GenAssets: Generating in-the-wild 3D Assets in Latent Space
Apr 24, 2026Ze Yang, Jingkang Wang, Haowei Zhang +33D Asset Generation Latent Diffusion Models
Waabi · University of Toronto
OccDirector: Language-Guided Behavior and Interaction Generation in 4D Occupancy Space
Apr 24, 2026Zhuding Liang, Tianyi Yan, Dubing Chen +6Diffusion Transformer Multi-Agent Systems
SKL-IOTSC, CIS, University of Macau, Macau, China · Li Auto Inc, Beijing, China
TRACE: Topology-aware Reconstruction of Accidents in CARLA for AV Evaluation
Apr 23, 2026Nahian Salsabil, Sebastian ElbaumAutonomous Driving Datasets Autonomous Driving Benchmarks
University of Virginia Charlottesville, Virginia, USA
OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge
Apr 22, 2026Yuhang Zhang, Jiarui Zhang, Bowen Jian +6Autonomous Driving Datasets Autonomous Driving Benchmarks
College of Transportation, Tongji University, Shanghai 201804, China.
X-Cache: Cross-Chunk Block Caching for Few-Step Autoregressive World Models Inference
Apr 22, 2026Yixiao Zeng, Jianlei Zheng, Chaoda Zheng +10Video Diffusion Models Diffusion Model Caching
AI Infra Team, XPeng Inc.
Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images
Apr 21, 2026Hongyuan Liu, Bochao Zou, Qiankun Liu +103D Asset Generation 3D Reconstruction
University of Science and Technology Beijing · Li Auto Inc.
From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing
Apr 20, 2026Linfeng Liang, Xiao Cheng, Tsong Yueh Chen +1Autonomous Driving Safety Evaluation Autonomous Driving Simulation
Macquarie University, Australia · Swinburne University of Technology, Australia
Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation
Apr 20, 2026Tianshi Cao, Jiawei Ren, Yuxuan Zhang +123D Asset Generation Sparse-View 3D Reconstruction
NVIDIA
ParkingScenes: A Structured Dataset for End-to-End Autonomous Parking in Simulation Scenes
Apr 20, 2026Haonan Chen, Kaiwen Xiao, Bin Tian +1Autonomous Driving Datasets End-to-End Autonomous Driving
Institute of Automation · Chinese Academy of Sciences · Beijing, China +3
ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation
Apr 18, 2026Lili Gao, Yanbo Xu, William Koch +8VLMs for Autonomous Driving 3D Scene Generation
1Torc Robotics · 2Princeton University · 3Mila
RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework
Apr 16, 2026Hao Gao, Shaoyu Chen, Yifan Zhu +4Diffusion-Based Planning RL for Diffusion Models
Huazhong University of Science & Technology · Horizon Robotics
Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Apr 14, 2026Saeed Rahmani, Shiva Rasouli, Daphne Cornelisse +3Autonomous Driving Simulation Human Behavior Simulation
Department of Transport & Planning, Delft University of Technology, Delft, the Netherlands. · Department of Industrial and Systems Engineering, University of Michigan, Dearborn, Michigan, USA · 3Tandon School of Engineering, New York University, New York, United States.
ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions
Apr 11, 2026Vivek Anand, Bharat Lohani, Rakesh Mishra +1Autonomous Driving Datasets Autonomous Driving Simulation
Geoinformatics Group, Department of Civil Engineering, Indian Institute of Technology Kanpur, India · Department of Geodesy and Geomatics Engineering, University of New Brunswick, NB, Canada · Department of Engineering Technology & Industrial Distribution (ETID) of Texas A&M University
Fail2Drive: Benchmarking Closed-Loop Driving Generalization
Apr 9, 2026Simon Gerstenecker, Andreas Geiger, Katrin RenzAutonomous Driving Benchmarks Autonomous Driving
University of Tübingen Tübingen AI Center
Compiling OpenSCENARIO 2.1 for Scenario-Based Testing in CARLA
Apr 7, 2026Thoshitha Gamage, Lasanthi GamageAutonomous Driving Simulation
Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL · Department of Computer Science, Webster University, Webster Groves, MO
Appearance Decomposition Gaussian Splatting for Multi-Traversal Reconstruction
Apr 7, 2026Yangyi Xiao, Siting Zhu, Baoquan Yang +33D Reconstruction Gaussian Splatting
Department of Automation, Key Laboratory of System Control and Information Processing of Ministry of Education, State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis, Shanghai Jiao Tong University, Shanghai 200240, China.
ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving
Apr 7, 2026Kaiser Hamid, Can Cui, Nade LiangRobustness of VLA Models Autonomous Driving Safety Evaluation
Texas Tech University · Bosch Center for Artificial Intelligence (BCAI)
Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
Apr 6, 2026Shiyao Qian, Yuan Ren, Dongfeng Bai +1Part-Level 3D Generation 3D Asset Generation
The authors are with Huawei Noah’s Ark Lab. · Shiyao Qian is with the University of Toronto and contributed to this work during an internship at Huawei Noah’s Ark Lab, Canada.
Efficient Equivariant Transformer for Self-Driving Agent Modeling
Apr 1, 2026Scott Xu, Dian Chen, Kelvin Wong +3Autonomous Driving Equivariant Neural Networks
Waabi · University of Toronto
Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models
Apr 1, 2026Xiaosong Jia, Yuqian Shao, Zhenjie Yang +3Autonomous Driving Benchmarks VLMs for Autonomous Driving
Institute of Trustworthy Embodied AI, Fudan University and Shanghai Key Laboratory of Multimodal Embodied AI, Shanghai, 200441, China. · School of Computer Science & School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200240, China.
Simulating Realistic LiDAR Data Under Adverse Weather for Autonomous Vehicles: A Physics-Informed Learning Approach
Apr 1, 2026Vivek Anand, Bharat Lohani, Rakesh Mishra +1Autonomous Driving Simulation Synthetic Data Augmentation
Geoinformatics Group, Department of Civil Engineering, Indian Institute of Technology Kanpur, India · Department of Geodesy and Geomatics Engineering, University of New Brunswick, NB, Canada · Department of Engineering Technology & Industrial Distribution (ETID) of Texas A&M University
Heterogeneous Self-Play for Realistic Highway Traffic Simulation
Mar 31, 2026Jinkai Qiu, Alessandro Saviolo, Chaojie Wang +2Autonomous Driving Simulation Autonomous Driving Scenario Generation
PlusAI · University of Michigan - Ann Arbor
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
Mar 30, 2026Mozhgan Pourkeshavarz, Tianran Liu, Nicholas RhinehartAutonomous Driving Simulation World Models for Autonomous Driving
University of Toronto, Canada
OccSim: Multi-kilometer Simulation with Long-horizon Occupancy World Models
Mar 30, 2026Tianran Liu, Shengwen Zhao, Mozhgan Pourkeshavarz +2Autonomous Driving Simulation World Models for Autonomous Driving
Learning, Embodied Autonomy, and Forecasting (LEAF) Lab, University of Toronto
CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence
Mar 30, 2026Tianle Zeng, Yanci Wen, Hong ZhangRobotics Simulation Autonomous Driving Simulation
1Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology.
Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehicles
Mar 26, 2026Pankaj Kumar, Pranamesh Chakraborty, Subrahmanya Swamy PeruruIntelligent Transportation Systems RL for Autonomous Driving
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur-208016, U.P., India · Department of Electrical Engineering Indian Institute of Technology Kanpur, Kanpur-208016, U.P., India

May 2, 2026J/K move · Enter open · S save

Dynamics Distillation for Efficient and Transferable Control Learning

Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian, Vladimir Suplin+1

University of Toronto · Vector Institute · NVIDIA+1

Vehicle Dynamics Transfer Learning RL for Autonomous Driving Autonomous Driving Simulation Sample-Efficient RL

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Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulators provide only in isolation. We introduce Sim2Sim2Sim, a framework that bridges high-fidelity vehicle simulation and scalable reinforcement learning by distilling simulator dynamics into a highly parallelizable learned dynamics model. By training control policies purely within this distilled environment and deploying them back into the high-fidelity source simulator, we demonstrate more efficient policy optimization and reliable transfer under challenging dynamics. We further show that predictive accuracy alone does not fully characterize a learned dynamics model's suitability as a reinforcement learning training environment, which should also be assessed by the quality of the policies it enables.