Autonomous Driving Scenario Generation

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8 papers in the last four weeks, up 60% on the four weeks before. 0.1% of all new papers.

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Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
May 6, 2026Zimu Gong, Brian Zhaoning Zhang, Chris Zhang +2Flow Matching Synthetic Data Generation for Robotics
University of Michigan-Ann Arbor · University of Waterloo · 1Waabi Innovation Inc +1
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
AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
Apr 21, 2026Jiagao Hu, Daiguo Zhou, Danzhen Fu +6Synthetic Data Augmentation Long-Horizon Video Generation
MiLM Plus, Xiaomi Inc. · Wuhan, China · Xiaomi Inc. +1
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
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
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
Nov 27, 2025Qiang Li, Yingwenqi Jiang, Tuoxi Li +173D Reconstruction Novel View Synthesis
1XPeng Motors · 2ShanghaiTech University · University of Science and Technology of China
LaGen: Towards Autoregressive LiDAR Scene Generation
Nov 26, 2025Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang +73D Scene Generation Autoregressive Generation
University of Science and Technology of China, Hefei, Anhui 230026, China · Shanghai Innovation Institute, Shanghai 200231, China · Fudan University, Shanghai 200433, China +1
Generative AI for Autonomous Driving: Frontiers and Opportunities
May 13, 2025Yuping Wang, Shuo Xing, Cui Can +46Autonomous Driving Autonomous Driving Scenario Generation
TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles
Aug 28, 2024Guanren Qiao, Guorui Quan, Jiawei Yu +2Autonomous Driving Simulation Autonomous Driving Scenario Generation
Chinese University of Hong Kong, Shenzhen, Guangdong, 518172 China · University of Manchester, Manchester, M13 9PL England · Shenyang MXNavi Co., Ltd, Shenyang, 110169 China

May 6, 2026, cs.ROJ/K move · Enter open · S save

Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation

Zimu Gong, Brian Zhaoning Zhang, Chris Zhang, Kelvin Wong+1

University of Michigan-Ann Arbor · University of Waterloo · 1Waabi Innovation Inc+1

Flow Matching Synthetic Data Generation for Robotics Conditional Variational Autoencoder Autonomous Driving Simulation Autonomous Driving Scenario Generation

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Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation offers a way to generate such scenarios, manually designed test cases lack scalability, and adversarial optimization often produces unrealistic behaviors. In this work, we introduce a conditional latent flow matching approach for scalable and realistic safety-critical scenario generation. Our method uses distribution matching to transform nominal scenes into safety-critical rollouts. Furthermore, we demonstrate that incorporating both simulation and real-world data enables our framework to efficiently generate diverse, data-driven scenarios. Experimental results highlight that our approach is able to more consistently and realistically generate novel safety-critical scenarios, making it a valuable tool for training and benchmarking AV systems.

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