Adversarial Scenario Generation

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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How corner is a corner case? Percentile control for highway scenario generation
Oct 4, 2026Jiaxi Liu, Hang Zhou, Hangyu Li +5Autonomous Driving Safety Evaluation Adversarial Scenario Generation
Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, 53706, WI, USA
Adversarial Closed-Loop Curriculum for Evolving Role-Playing Agents
Sep 23, 2026Zheng Zhang, Liu Liu, Qi Chai +4Large Language Model-Based Role-Play Simulation Adversarial Scenario Generation
The Hong Kong University of Science and Technology (Guangzhou) · Tencent · Shanghai Jiao Tong University
Teach-to-Crash: A Closed-Loop Student-Teacher LLM Framework for Collision-Inducing Test Scenario Generation
Sep 23, 2026Zaid Ghazal, Khouloud Gaaloul, Bruce MaximAutonomous Driving Safety Evaluation Adversarial Scenario Generation
University of Michigan-Dearborn Dearborn, MI, USA
Safety-Critical Scenanrio Emerges from Initial Scene
Sep 17, 2026Yin Wu, Jiarong Wei, Carl Esselborn +4Adversarial Scenario Generation Autonomous Driving Simulation
CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation
Sep 2, 2026Shucheng Zhang, Yuang Zhang, Bingzhang Wang +3Autonomous Driving Scenario Generation Adversarial Scenario Generation
Department of Civil and Environmental Engineering, University of Washington Seattle, WA 98195, USA
ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents
Aug 12, 2026Yutao Mou, Pengfei Yang, Zhe Yin +6AI Agent Evaluation Adversarial Scenario Generation
National Engineering Research Center for Software Engineering, Peking University · Harbin Institute of Technology · Beijing University of Posts and Telecommunications +1
Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning
Aug 11, 2026Xincong Hu, Lei Ou, Maosen Li +3Adversarial Scenario Generation Safe RL
Nanjing University · 1Nanjing University, Nanjing, Jiangsu, China · 2Yinwang Intelligent Technology Co., Ltd., China +1
Do LLMs Know Their Vulnerable Scenarios?
Jul 26, 2026Ziheng Peng, Huiqi Deng, Haoran Jing +5LLM Interpretability Adversarial Scenario Generation
Xi’an Jiaotong University Shanghai, China · Shanghai Artificial Intelligence Laboratory Shanghai, China
Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation
Jul 15, 2026Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan +5Multimodal Robustness Adversarial Scenario Generation
1UCLA · *Work done as a student researcher at Google. · 2Google
Seduced by the Narrative: Assessing Rule Adherence in Semi-Open Textual Sandboxes
Jul 2, 2026Weiying Chen, Junlong Shen, Zhanyuan Guo +1LLM Safety Benchmarks Adversarial Prompt Generation
University of Alberta, Edmonton, AB, Canada · N-Dice Association · Qilu University of Technology, Shandong, China
AdversaBench: Automated LLM Red-Teaming with Multi-Judge Confirmation and Cross-Model Transferability
Jun 23, 2026Khanak KhandelwalAdversarial Prompt Generation Adversarial Attacks on LLMs
Indian Institute of Technology Jodhpur
PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models
Jun 23, 2026Simone Gallivanone, Hossein Khodadadi, Mauro Dore +2VLM Evaluation VLM Robustness
The Italian Institute of Artificial Intelligence (AI4I), Turin, Italy · HikmaAI S.r.l., Pula, Italy
SPHINX: First Explain, Then Explore
Jun 16, 2026Nguyen Do, Tue M. Cao, Tien Van Do +3Autonomous Driving Safety Evaluation Adversarial Robustness
University of Florida, Gainesville, Florida, USA · Budapest University of Technology and Economics, Department of Networked Systems and Services, Budapest, Hungary · University of Debrecen, Debrecen, Hungary
Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
Jun 4, 2026Nikolai Dorofeev, Alexey Odinokov, Rostislav YavorskiyRobot Policy Learning Adversarial Scenario Generation
SafePI.ai Grenoble, France · SafePI.ai Belgrade, Serbia · SafePI.ai Madrid, Spain
EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
Jun 2, 2026Tong Nie, Yuewen Mei, Yihong Tang +4Multi-Objective Evolutionary Optimization Autonomous Driving Safety Evaluation
The Hong Kong Polytechnic University · Tongji University · McGill University +1
ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving
May 20, 2026Qiyu Ruan, Yuxuan Wang, He Li +2Autonomous Driving Safety Evaluation Adversarial Scenario Generation
State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau, Macau, China · Faculty of Science and Technology, University of Macau, Macau, China
KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution
May 17, 2026Cheng Wang, Chen Xiong, Ziwen Wang +2Autonomous Driving Safety Evaluation Adversarial Scenario Generation
Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment
May 15, 2026Chuancheng Zhang, Zhenhao Wang, Kaizheng Li +3Autonomous Driving Safety Evaluation Synthetic Data Generation
Shenzhen Research Institute of Shandong University, Shenzhen, 518057, Guangdong, China · School of Airspace Science and Engineering, Shandong University, 264209 Weihai, China. · School of Computer Science and Technology, Shandong University of Finance and Economics, 250014, Jinan, China.
Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles
May 13, 2026Yizhuo Xiao, Haotian Yan, Ying Wang +5AI Accountability Autonomous Driving Safety Evaluation
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K. · State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, China · College of Computer Science and Technology, Jilin University, Changchun, China +3
MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety
May 3, 2026Jialin Song, Xiaodong Liu, Weiwei Yang +4LLM Safety Benchmarks Adversarial Prompt Generation
Simon Fraser University · Microsoft Research
Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF
Apr 20, 2026Yuan Fang, Yiming Luo, Aimin Zhou +1Language Model Safety Evaluation Adversarial Scenario Generation
East China Normal University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China

Oct 4, 2026, cs.AIJ/K move · Enter open · S save

How corner is a corner case? Percentile control for highway scenario generation

Jiaxi Liu, Hang Zhou, Hangyu Li, Yifan Wang+4

Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, 53706, WI, USA

Autonomous Driving Safety Evaluation Adversarial Scenario Generation Autonomous Driving Scenario Generation Diffusion Models

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Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they provide limited control over how extreme a generated scenario is relative to plausible futures in the same traffic context. This study represents the adversity of a generated scenario as its percentile in the conditional distribution of future risk given the observed history. This view supports calibrated answers to two questions: how "corner" a generated corner-case scenario is and how its "cornerness" can be fine-tuned. To this end, we formulate history-conditioned risk-percentile requests and learn a reference risk distribution that maps each requested percentile to a physical risk target. We then use a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures, together with a reference-based criterion for evaluating percentile realization. Experiments use the minimum post-encroachment time (PET) between the ego and its surrounding vehicles as the risk surrogate on highD. On the primary evaluation set, our method realizes 1,422 of 1,440 requests within a 0.05 percentile tolerance (98.75%), with mean percentile error 0.00673 and PET-target error 0.00991 seconds. The resulting interface connects context-relative risk specification, physical realization, and evaluation through a common risk scale. Project website and videos of generated scenarios are available at https://hhj233.github.io/CornerPercentile/.

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