{"total":105,"rows":[{"id":"2310.12432","title":"CAT: Closed-loop Adversarial Training for Safe End-to-End Driving","created":"2023-10-19","cited_by_count":54,"area":"driving"},{"id":"2401.00391","title":"SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries","created":"2023-12-31","cited_by_count":38,"area":"diffusion"},{"id":"2506.11526","title":"Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis","created":"2025-06-13","cited_by_count":38,"area":"driving"},{"id":"2312.00812","title":"Empowering Autonomous Driving with Large Language Models: A Safety Perspective","created":"2023-11-28","cited_by_count":34,"area":"driving"},{"id":"2211.00385","title":"Behavioral Intention Prediction in Driving Scenes: A Survey","created":"2022-11-01","cited_by_count":30,"area":null},{"id":"2412.07689","title":"RoboTron-Drive: All-in-One Large Multimodal Model for Autonomous Driving","created":"2024-12-10","cited_by_count":30,"area":"mllm"},{"id":"2311.11056","title":"Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving","created":"2023-11-18","cited_by_count":26,"area":null},{"id":"2412.17920","title":"Causal Composition Diffusion Model for Closed-loop Traffic Generation","created":"2024-12-23","cited_by_count":22,"area":"diffusion"},{"id":"2410.08453","title":"AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion","created":"2024-10-11","cited_by_count":20,"area":"diffusion"},{"id":"2404.08570","title":"Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation","created":"2024-04-12","cited_by_count":19,"area":"driving"},{"id":"2501.15850","title":"LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models","created":"2025-01-27","cited_by_count":19,"area":"driving"},{"id":"2603.01063","title":"Unleashing VLA Potentials in Autonomous Driving via Explicit Learning from Failures","created":"2026-03-01","cited_by_count":19,"area":"vla"},{"id":"2310.02815","title":"CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity","created":"2023-10-04","cited_by_count":18,"area":null},{"id":"2311.10747","title":"Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving","created":"2023-10-31","cited_by_count":17,"area":"driving"},{"id":"2305.07497","title":"Dynamically Conservative Self-Driving Planner for Long-Tail Cases","created":"2023-05-12","cited_by_count":13,"area":null},{"id":"2406.02983","title":"FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality","created":"2024-06-05","cited_by_count":13,"area":null},{"id":"2502.19652","title":"Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning","created":"2025-02-27","cited_by_count":13,"area":"deeprl"},{"id":"2502.15119","title":"CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models","created":"2025-02-21","cited_by_count":12,"area":"mllm"},{"id":"2405.03807","title":"UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarios","created":"2024-05-06","cited_by_count":10,"area":null},{"id":"2409.07569","title":"A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges","created":"2024-09-11","cited_by_count":10,"area":"deeprl"},{"id":"2409.10320","title":"SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation","created":"2024-09-16","cited_by_count":10,"area":"driving"},{"id":"2505.11247","title":"LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios","created":"2025-05-16","cited_by_count":10,"area":"diffusion"},{"id":"2409.00858","title":"Trustworthy Human-AI Collaboration: Reinforcement Learning with Human Feedback and Physics Knowledge for Safe Autonomous Driving","created":"2024-09-01","cited_by_count":8,"area":"driving"},{"id":"2502.02145","title":"From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios","created":"2025-02-04","cited_by_count":8,"area":"driving"},{"id":"2307.07083","title":"A Scenario-Based Functional Testing Approach to Improving DNN Performance","created":"2023-07-13","cited_by_count":7,"area":null},{"id":"2503.05808","title":"DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models","created":"2025-03-04","cited_by_count":7,"area":null},{"id":"2505.00972","title":"Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models","created":"2025-05-02","cited_by_count":7,"area":"rag"},{"id":"2305.13960","title":"From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving","created":"2023-05-23","cited_by_count":6,"area":null},{"id":"2309.09317","title":"Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling","created":"2023-09-17","cited_by_count":5,"area":null},{"id":"2507.13729","title":"AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework","created":"2025-07-18","cited_by_count":5,"area":"agents"},{"id":"2309.14209","title":"Continual Driving Policy Optimization with Closed-Loop Individualized Curricula","created":"2023-09-25","cited_by_count":4,"area":"driving"},{"id":"2412.08053","title":"DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time","created":"2024-12-11","cited_by_count":4,"area":null},{"id":"2505.00515","title":"Safety-Critical Traffic Simulation with Guided Latent Diffusion Model","created":"2025-05-01","cited_by_count":4,"area":"diffusion"},{"id":"2505.03344","title":"RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation","created":"2025-05-06","cited_by_count":4,"area":null},{"id":"2505.13466","title":"AgentSGEN: Multi-Agent LLM in the Loop for Semantic Collaboration and GENeration of Synthetic Data","created":"2025-05-07","cited_by_count":4,"area":"agents"},{"id":"2509.16950","title":"Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving","created":"2025-09-21","cited_by_count":4,"area":"safety"},{"id":"2603.21104","title":"CounterScene: Counterfactual Causal Reasoning in Generative World Models for Safety-Critical Closed-Loop Evaluation","created":"2026-03-22","cited_by_count":4,"area":"wm"},{"id":"2309.14235","title":"Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving","created":"2023-09-25","cited_by_count":3,"area":"driving"},{"id":"2403.17805","title":"Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving","created":"2024-03-26","cited_by_count":3,"area":null},{"id":"2405.11718","title":"Feasibility Consistent Representation Learning for Safe Reinforcement Learning","created":"2024-05-20","cited_by_count":3,"area":"deeprl"},{"id":"2407.15839","title":"Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments","created":"2024-07-22","cited_by_count":3,"area":"deeprl"},{"id":"2411.17826","title":"Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling","created":"2024-11-26","cited_by_count":3,"area":null},{"id":"2503.15049","title":"HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation","created":"2025-03-19","cited_by_count":3,"area":"driving"},{"id":"2505.15863","title":"Generative AI for Autonomous Driving: A Review","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2505.24808","title":"RealDrive: Retrieval-Augmented Driving with Diffusion Models","created":"2025-05-30","cited_by_count":3,"area":"rag"},{"id":"2508.02269","title":"AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models","created":"2025-08-04","cited_by_count":3,"area":null},{"id":"2508.19882","title":"Generative AI for Testing of Autonomous Driving Systems: A Survey","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2510.04978","title":"Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI","created":"2025-10-06","cited_by_count":3,"area":"wm"},{"id":"2510.12206","title":"Controllable Collision Scenario Generation via Collision Pattern Prediction","created":"2025-10-14","cited_by_count":3,"area":null},{"id":"2603.15221","title":"ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving","created":"2026-03-16","cited_by_count":3,"area":"driving"},{"id":"2406.12635","title":"ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation","created":"2024-06-18","cited_by_count":2,"area":null},{"id":"2407.04359","title":"Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2504.10296","title":"Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving","created":"2025-04-14","cited_by_count":2,"area":null},{"id":"2505.22067","title":"From Failures to Fixes: LLM-Driven Scenario Repair for Self-Evolving Autonomous Driving","created":"2025-05-28","cited_by_count":2,"area":"synth"},{"id":"2603.04071","title":"SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling","created":"2026-03-04","cited_by_count":2,"area":"driving"},{"id":"2408.14000","title":"Quantitative Representation of Scenario Difficulty for Autonomous Driving Based on Adversarial Policy Search","created":"2024-08-26","cited_by_count":1,"area":"driving"},{"id":"2408.15538","title":"TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles","created":"2024-08-28","cited_by_count":1,"area":"driving"},{"id":"2412.00517","title":"LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization","created":"2024-11-30","cited_by_count":1,"area":null},{"id":"2412.02574","title":"Generating Critical Scenarios for Testing Automated Driving Systems","created":"2024-12-03","cited_by_count":1,"area":"driving"},{"id":"2503.00077","title":"Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2503.11771","title":"Controllable Latent Diffusion for Traffic Simulation","created":"2025-03-14","cited_by_count":1,"area":"diffusion"},{"id":"2503.23708","title":"Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios","created":"2025-03-31","cited_by_count":1,"area":"driving"},{"id":"2504.17792","title":"My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data","created":"2025-04-10","cited_by_count":1,"area":null},{"id":"2508.11514","title":"DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality","created":"2025-08-15","cited_by_count":1,"area":null},{"id":"2512.02844","title":"VLM as Strategist: Adaptive Generation of Safety-critical Testing Scenarios via Guided Diffusion","created":"2025-12-02","cited_by_count":1,"area":"diffusion"},{"id":"2512.07874","title":"Controllable risk scenario generation from human crash data for autonomous vehicle testing","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2512.11887","title":"Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions","created":"2025-12-09","cited_by_count":1,"area":"driving"},{"id":"2602.13248","title":"X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2603.02542","title":"AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation","created":"2026-03-03","cited_by_count":1,"area":"diffusion"},{"id":"2607.14387","title":"Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving","created":"2026-07-15","cited_by_count":1,"area":"rag"},{"id":"2608.11592","title":"Video2Track: From Real-World Interaction Videos to Steerable Adversarial Closed-Track Testing for Automated Driving Systems","created":"2026-08-12","cited_by_count":1,"area":"driving"},{"id":"2609.08965","title":"PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving","created":"2026-09-08","cited_by_count":1,"area":"driving"},{"id":"2306.07142","title":"Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles","created":"2023-06-12","cited_by_count":0,"area":null},{"id":"2309.06313","title":"Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle","created":"2023-09-12","cited_by_count":0,"area":null},{"id":"2406.02865","title":"Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Framework","created":"2024-06-05","cited_by_count":0,"area":"driving"},{"id":"2407.00980","title":"Acceleration method for generating perception failure scenarios based on editing Markov process","created":"2024-07-01","cited_by_count":0,"area":null},{"id":"2503.00815","title":"Evaluation of adaptive sampling methods in scenario generation for virtual safety impact assessment of pre-crash safety systems","created":"2025-03-02","cited_by_count":0,"area":null},{"id":"2507.15587","title":"Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario","created":"2025-07-21","cited_by_count":0,"area":null},{"id":"2507.22429","title":"Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2510.06970","title":"Falsification-driven reinforcement learning for maritime motion planning","created":"2025-10-08","cited_by_count":0,"area":"deeprl"},{"id":"2510.24949","title":"SCOUT: A Lightweight Framework for Scenario Coverage Assessment in Autonomous Driving","created":"2025-10-28","cited_by_count":0,"area":"mllm"},{"id":"2512.01363","title":"SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2603.04819","title":"On the Strengths and Weaknesses of Data for Open-set Embodied Assistance","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2603.20234","title":"Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation","created":"2026-03-08","cited_by_count":0,"area":"deeprl"},{"id":"2604.12857","title":"Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2605.04366","title":"Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.13751","title":"Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles","created":"2026-05-13","cited_by_count":0,"area":"driving"},{"id":"2605.21168","title":"ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving","created":"2026-05-20","cited_by_count":0,"area":"driving"},{"id":"2606.18532","title":"AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2606.21993","title":"From Driving Videos to Simulatable Scenarios","created":"2026-06-20","cited_by_count":0,"area":"driving"},{"id":"2607.04451","title":"CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation","created":"2026-07-05","cited_by_count":0,"area":null},{"id":"2607.12214","title":"Robust 4D Driving Scene Reconstruction from Imperfect Visual Priors","created":"2026-07-07","cited_by_count":0,"area":"wm"},{"id":"2607.18637","title":"End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2608.00298","title":"WM-Cov: Test Adequacy for Interactive World-Model-Style Autonomous Driving Simulation","created":"2026-07-31","cited_by_count":0,"area":"wm"},{"id":"2608.04776","title":"NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.30179","title":"Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models","created":"2026-08-31","cited_by_count":0,"area":"driving"},{"id":"2609.00718","title":"A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies","created":"2026-09-01","cited_by_count":0,"area":"driving"},{"id":"2609.04921","title":"One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation","created":"2026-09-04","cited_by_count":0,"area":"diffusion"},{"id":"2609.06433","title":"Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2609.19903","title":"REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Models","created":"2026-09-17","cited_by_count":0,"area":"driving"}],"next_cursor":"100"}