{"total":47,"rows":[{"id":"2101.06549","title":"AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles","created":"2021-01-16","cited_by_count":137,"area":null},{"id":"2304.12205","title":"Synthetic Datasets for Autonomous Driving: A Survey","created":"2023-04-24","cited_by_count":48,"area":null},{"id":"2110.04471","title":"Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning","created":"2021-10-09","cited_by_count":29,"area":null},{"id":"2209.13679","title":"V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception","created":"2022-09-27","cited_by_count":28,"area":null},{"id":"2103.07403","title":"Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles","created":"2021-03-12","cited_by_count":21,"area":null},{"id":"2112.00964","title":"A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation","created":"2021-12-02","cited_by_count":21,"area":null},{"id":"2110.13939","title":"CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation","created":"2021-10-26","cited_by_count":20,"area":null},{"id":"2103.07932","title":"Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems","created":"2021-03-14","cited_by_count":18,"area":null},{"id":"2306.14131","title":"Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing","created":"2023-06-25","cited_by_count":16,"area":null},{"id":"2110.08664","title":"Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review","created":"2021-10-16","cited_by_count":12,"area":null},{"id":"2210.01368","title":"RAP: Risk-Aware Prediction for Robust Planning","created":"2022-10-04","cited_by_count":12,"area":null},{"id":"2109.06404","title":"Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems","created":"2021-09-14","cited_by_count":8,"area":null},{"id":"2209.09674","title":"Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models","created":"2022-09-20","cited_by_count":7,"area":null},{"id":"2102.07060","title":"Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers","created":"2021-02-14","cited_by_count":6,"area":null},{"id":"2105.08846","title":"Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education","created":"2021-05-18","cited_by_count":6,"area":null},{"id":"2505.17966","title":"Is Single-View Mesh Reconstruction Ready for Robotics?","created":"2025-05-23","cited_by_count":6,"area":null},{"id":"2510.10020","title":"Calibrating Generative Models to Distributional Constraints","created":"2025-10-11","cited_by_count":6,"area":null},{"id":"2108.06898","title":"Neural-to-Tree Policy Distillation with Policy Improvement Criterion","created":"2021-08-16","cited_by_count":5,"area":null},{"id":"2111.09884","title":"Assisted Robust Reward Design","created":"2021-11-18","cited_by_count":5,"area":null},{"id":"2505.21743","title":"Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen","created":"2025-05-27","cited_by_count":5,"area":null},{"id":"2202.13510","title":"Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems","created":"2022-02-28","cited_by_count":4,"area":null},{"id":"2304.00365","title":"Adaptive Failure Search Using Critical States from Domain Experts","created":"2023-04-01","cited_by_count":4,"area":null},{"id":"2111.02204","title":"Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems","created":"2021-11-03","cited_by_count":3,"area":null},{"id":"2112.00206","title":"Querying Labelled Data with Scenario Programs for Sim-to-Real Validation","created":"2021-12-01","cited_by_count":3,"area":null},{"id":"2202.03651","title":"Causal Scene BERT: Improving object detection by searching for challenging groups of data","created":"2022-02-08","cited_by_count":3,"area":null},{"id":"2305.09930","title":"Model-based Validation as Probabilistic Inference","created":"2023-05-17","cited_by_count":3,"area":null},{"id":"2310.19167","title":"Rare Event Probability Learning by Normalizing Flows","created":"2023-10-29","cited_by_count":3,"area":null},{"id":"2106.10566","title":"Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling","created":"2021-06-19","cited_by_count":2,"area":null},{"id":"2110.02892","title":"Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios","created":"2021-10-06","cited_by_count":2,"area":null},{"id":"2307.07666","title":"Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty","created":"2023-07-15","cited_by_count":2,"area":null},{"id":"2101.10377","title":"Learning to falsify automated driving vehicles with prior knowledge","created":"2021-01-25","cited_by_count":1,"area":null},{"id":"2502.21110","title":"Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?","created":"2025-02-28","cited_by_count":1,"area":null},{"id":"2104.00859","title":"An NCAP-like Safety Indicator for Self-Driving Cars","created":"2021-04-02","cited_by_count":0,"area":null},{"id":"2110.05390","title":"Synthesizing Machine Learning Programs with PAC Guarantees via Statistical Sketching","created":"2021-10-11","cited_by_count":0,"area":null},{"id":"2210.10876","title":"Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale","created":"2022-10-19","cited_by_count":0,"area":null},{"id":"2301.01594","title":"Finding Needles in Haystack: Formal Generative Models for Efficient Massive Parallel Simulations","created":"2023-01-03","cited_by_count":0,"area":null},{"id":"2308.13658","title":"Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs","created":"2023-08-25","cited_by_count":0,"area":null},{"id":"2405.15771","title":"Adaptive Splitting of Reusable Temporal Monitors for Rare Traffic Violations","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2409.08097","title":"Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach","created":"2024-09-12","cited_by_count":0,"area":null},{"id":"2411.03328","title":"Foundation Models for Rapid Autonomy Validation","created":"2024-10-22","cited_by_count":0,"area":null},{"id":"2505.13335","title":"Scalable Importance Sampling in High Dimensions with Low-Rank Mixture Proposals","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2605.15134","title":"Training ML Models with Predictable Failures","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.16313","title":"Is Your Trajectory Displacement Safe in Long-tail?","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.31114","title":"Revealing Safety-Critical Scenarios for UTM via Transformer","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2606.31131","title":"Scenario Generation for Testing of Autonomous Driving Systems Using Real-World Failure Records","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2609.06433","title":"Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2610.05003","title":"How corner is a corner case? Percentile control for highway scenario generation","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}